System and method for management and prediction of invasive mechanical ventilation necessity

Abstract

The invention relates to a method for monitoring invasive mechanical ventilation, the method comprising: retrieving at least one data from at least one data source, preprocessing the at least one data to automatically generate at least one preprocessed data, categorizing the at least one preprocessed data into at least one invasive mechanical ventilation status data, and activating at least one artificial-intelligence-assisted module, wherein the activating of the at least one artificial-intelligence-assisted module is based on the at least one invasive mechanical ventilation status data The invention further relates to a system for monitoring invasive mechanical ventilation, the system comprising a retrieving component configured to retrieve at least one data from at least one data source, a processing component configured to at least preprocess the at least one data to automatically generate at least one preprocessed data, a categorizing component configured to categorize the at least one preprocessed data into at least one invasive mechanical ventilation status data, and an activating component configured to activate at least one artificial-intelligence-assisted module, wherein the activating component may be configured to activate the at least one artificial-intelligence-assisted module based on the at least one invasive mechanical ventilation status data.

Description

[0001] System and method for management and prediction of invasive mechanical ventilation necessityField The invention lies in the field of monitoring health status and particularly in the field ofmonitoring invasive mechanical ventilation necessity of humans. The goal of the presentinvention is to provide a system and method for monitoring the health status of humans with respect to invasive mechanical ventilation necessity. More particularly, the present invention relates to a system, a method performed in such a system and corresponding use of a system for management and prediction of invasive mechanical ventilation necessity. Introduction Invasive Mechanical Ventilation (IMV) is one of the most frequently used organ supportmethods in intensive care units (ICUs). Routinely collected electronic health data (EHR),powering machine learning tools, may aid in assessing patient necessity when deciding toadminister or discontinue IMV. Among the different aspects of IMV management, timely and successful weaning from IMV as well as timely detection and prediction of the necessity for IMV, are paramount.Several attempts to employ machine learning to optimize IMV have been published(10.1016 / j.bja.2021.09.025), but few reported the use of ML-based clinical decisionsupport systems for predicting necessity of IMV; which may comprise weaning readinessas well as requirement of IMV. Current ventilation management systems integrate sophisticated technologies such as model-based optimization, early warning scoring systems, decision-making methods, and weaning timing prediction. These systems utilize computational models to simulate patient- specific respiratory mechanics, continuously monitor physiological parameters, predict respiratory deterioration, optimize ventilator settings, and accurately time weaning from mechanical ventilation. By leveraging real-time data analytics and machine learning algorithms, these systems aim to enhance the efficiency, efficacy, and safety of invasive mechanical ventilation management. While they represent a significant advancement in critical care technology, invasive mechanical ventilation system management and prediction is still lacking, especially when the prediction relates to the necessity of the IMV as well as the weaning of IMV. CN116913468A relates to a noninvasive method and system for providing early warnings regarding the need for invasive mechanical ventilation. The system continuously monitors noninvasive physiological parameters such as respiratory rate, heart rate, oxygen saturation, and other relevant indicators. These parameters are analyzed using sophisticated algorithms to assess the patient's respiratory status and predict the potentialneed for invasive mechanical ventilation. When the analysis indicates a high risk ofrespiratory failure or the imminent need for mechanical support, the system generates an early warning alert. This allows healthcare providers to take proactive measures, potentially improving patient outcomes by initiating timely interventions and preventing the escalation of respiratory distress. The invention aims to enhance patient care in various clinical settings, including intensive care units and emergency departments. WO 2016 / 103142 A1 outlines systems and methods for the model-based optimization of mechanical ventilation. The invention focuses on improving the management of mechanical ventilation for patients by using a computational model that simulates patient-specific respiratory mechanics and responses to various ventilator settings. This model-based approach allows for the continuous adjustment and optimization of ventilator parameters such as tidal volume, respiratory rate, and pressure settings to match the patient’s evolving condition. By incorporating real-time data and predictive algorithms, the system aims to enhance the efficiency and efficacy of ventilation therapy, reduce the risk of ventilator- induced lung injury, and improve overall patient outcomes. CN114259633A describes a mechanical ventilation decision method and device, along with associated storage medium and electronic equipment. This invention focuses on providing an intelligent system for making informed decisions regarding the management of mechanical ventilation in patients. It utilizes a comprehensive set of data inputs, including patient-specific physiological parameters and clinical indicators, which are processed through advanced algorithms. The system evaluates the patient’s current respiratory status and predicts future trends to determine the most appropriate ventilator settings. The device can automatically adjust parameters such as tidal volume, respiratory rate, and pressure levels to optimize ventilation support. Additionally, the system includes a storage medium for recording data and decision processes, as well as electronic equipment for interfacing with healthcare providers. US 2022 / 0233799 A1 pertains to a ventilator-weaning timing prediction system, including a program product and methods for building and using the system. The invention aims to accurately predict the optimal timing for weaning patients off mechanical ventilation. This system leverages machine learning algorithms trained on historical patient data to identify patterns and indicators that signify readiness for weaning. It integrates various patient- specific parameters such as respiratory function, vital signs, and other clinical data to generate a predictive model. The system provides real-time recommendations tohealthcare providers, enhancing decision-making processes related to weaning. Byoptimizing the timing of weaning, the system seeks to reduce the risks associated with prolonged ventilation, such as ventilator-associated pneumonia, and improve patient recovery times and outcomes. Summary In light of the above, it is therefore an object of the present invention to overcome or at least to alleviate the shortcomings and disadvantages of the prior art. More particularly, it is an object of the present invention to provide a more accurate and less prone to failure method and a corresponding system for detection and prediction of invasive mechanical ventilation necessity. These objects are met by the present invention.In a first aspect, the invention relates to a method for monitoring invasive mechanicalventilation, the method comprising: retrieving at least one data from at least one datasource, preprocessing the at least one data to automatically generate at least onepreprocessed data, categorizing the at least one preprocessed data into at least oneinvasive mechanical ventilation status data, and activating at least one artificial-intelligence-assisted module, wherein the activating of the at least one artificial- intelligence-assisted module is based on the at least one invasive mechanical ventilationstatus data.The at least one invasive mechanical ventilation status data references the status of the invasive mechanical ventilation system.In one embodiment, the preprocess the preprocessing step may comprise performing atleast one measurement unit conversion. In another embodiment, the preprocessing stepmay comprise discarding at least one out-of-range value. In a further embodiment, thepreprocessing step may comprise processing at least one ventilator parameter data.Additionally or alternatively, the preprocessing step may comprise processing at least oneblood chemistry data. Moreover, the preprocessing step may comprise processing at leastone physiological data.Moreover, the at least one artificial-intelligence assisted module may comprise at least oneof: a first module, a second module, a third module, a fourth module, a fifth module, asixth module, a seventh module, and an eighth module.In one embodiment, the categorizing step may comprise performing the categorizing stepaccording to the at least one ventilator parameter data.Further, the method may comprise determining which of the at least one artificial-intelligence-assisted module to activate. The method may further comprise the determiningstep of which at least one artificial-intelligence-assisted module to activate, precedes thestep of activating the at least one artificial-intelligence-assisted module.Moreover, the method may comprise activating a plurality of artificial-intelligence-assistedmodules. The method may comprise determining which combination of the at least oneartificial-intelligence-assisted modules to activate. The method determining which at leastone artificial-intelligence-assisted module to activate, precedes the step of activating thecombination of the at least one artificial-intelligence-assisted modules. In one embodiment,determining which at least one artificial-intelligence-assisted module to activate may bebased on the at least one invasive mechanical ventilation status data.In one embodiment, when the least one invasive mechanical ventilation status datacomprises data indicating an active invasive mechanical ventilation, any combination of thefirst module, the second module, the third module and the fourth module can be activated.In another embodiment, when the least one invasive mechanical ventilation status datamay comprise data indicating an inactive invasive mechanical ventilation, any combinationof the fifth module, the sixth module, the seventh module and the eighth module can beactivated. Furthermore, the at least one artificial-intelligence-assisted module may comprisepredicting a future invasive mechanical ventilation weaning trajectory. In a furtherembodiment, the at least one artificial-intelligence-assisted module may comprisepredicting a future invasive mechanical ventilation necessity trajectory. The method maycomprise predicting at least one probability of successful weaning from invasive mechanicalventilation, wherein successful weaning from mechanical ventilation may be defined asconsecutive 48-hours without mechanical ventilation. The method may comprise predictingat least one probability of initiating invasive mechanical ventilation.In another embodiment, the method may comprise predicting at least one number of hoursbefore successful weaning from mechanical ventilation at the current time. The methodmay comprise predicting at least one number of hours before initiating mechanicalventilation at the current time. The predicting may be based on the at least one invasivemechanical ventilation status data. The predicting may also be performed by the at leastone artificial-intelligence assisted module. The predicting may further be based on the at least one preprocessed data.In a further embodiment, the first module may comprise predicting at least one probabilityof successful weaning from mechanical ventilation within 24 hours at the current time. Thesecond module may comprise predicting at least one probability of successful weaning frommechanical ventilation within 48 hours at the current time. The third module may comprisepredicting at least one probability of successful weaning from mechanical ventilation within72 hours at the current time. The fourth module may comprise predicting at least onenumber of hours before successful weaning from mechanical ventilation at the currenttime. The fifth module may comprise predicting at least one probability of initiatingmechanical ventilation within 24 hours at the current time. The sixth module may comprisepredicting at least one probability of initiating mechanical ventilation within 48 hours at thecurrent time. The seventh module may comprise predicting at least one probability ofinitiating mechanical ventilation within 72 hours at the current time. The eighth modulemay comprise predicting at least one number of hours before initiating mechanicalventilation at the current time. The predicting steps may be performed hourly Further, the method may comprise automatically generating at least one successfulweaning probability level, wherein the at least one successful weaning probability levelexpresses a lack of necessity for a patient’s invasive mechanical ventilation. The successfulweaning probability level may be based on the predicting of a future invasive mechanicalventilation weaning trajectory. The method may also comprise automatically generating atleast one risk level, wherein the at least one risk level expresses a necessity for a patient’sinvasive mechanical ventilation. The risk level may be based on the predicting of a futureinvasive mechanical ventilation necessity trajectory.In another embodiment, the method may comprise outputting at least one decision-supporting-data based on the predicting of the invasive mechanical ventilation weaningtrajectory. The method may comprise outputting at least one decision-supporting-databased on the predicting of the invasive mechanical ventilation necessity trajectory.Moreover, the at least one preprocessed data may be categorized in at least one zonewherein further the at least one zone may be delimited according to at least one range of values, wherein the at least one range of values may be defined, with respect to astandardized database, to assess the necessity for invasive mechanical ventilation. The atleast one preprocessed data may be categorized in at least one zone wherein further theat least one zone may be delimited according to at least one range of values, wherein the at least one range of values may be defined, with respect to a standardized database, to assess readiness for a Spontaneous Breathing Trial.Additionally, the at least one preprocessing data may be classified in at least one zonewherein further the at least one zone may be delimited according to at least one range ofvalues, wherein the at least one range of values may be defined, with respect to astandardized database, to assess if a Spontaneous Breathing Trial is successful. The atleast one preprocessing data may be classified in at least one zone wherein further the atleast one zone may be delimited according to at least one range of values, wherein the atleast one range of values may be defined, with respect to a standardized database, toassess readiness for extubation. The at least one preprocessing data may further beclassified in at least one zone wherein further the at least one zone may be delimitedaccording to at least one range of values, wherein the at least one range of values, whereinthe at least one range of values may be defined, with respect to a standardized database,to assess readiness for weaning from mechanical ventilation. The method may further comprise outputting at least one signal related to the at least one preprocessed data withrespect to the at least one zone it may be classified in, such as but not limited to activatingan alert according to the urgency of the at least one zone and / or the combination of zones. The alert may also relate to the combination of a successful Spontaneous Breathing Trialand readiness extubation. The alert may be related to at least one metric exceedingpredefined clinical safety thresholds, such as but not limited to threshold for driving pressure, and / or mechanical power, wherein the at least one metric may be associated with risk of ventilator induced lung injury (VILI).Furthermore, the method may comprise displaying the at least one preprocessed data withrespect to the at least one zone it may be categorized in. The method may also comprisedisplaying at least one successful weaning probability level and / or at least one risk level in function of time.In one embodiment, the method may comprise storing and displaying at least one inputsubmitted by an authorized user corresponding to the at least one successful weaningprobability level and / or the at least one risk level at at least one time. That input maycomprise preferably a note, a message… that may be related to the data or the time the input is corresponding to.In another embodiment, the method may comprise prompting at least one authorized userto input at least one data point. The at least one data point may comprise computer-readable input data points. The at least one data point may comprise at least one selectabledata point comprising at least one predetermined select data point, wherein the methodmay comprise prompting at least one authorized user to select at least one option. Themethod may comprise bidirectionally communicating with at least one server. For example,a user-interface may be implemented as a web-based application and / or progressive web app accessible via at least one end-user device.Further, the method may comprise utilizing any data utilized in the method as describedherein to train at least one algorithm. The training step may be performed on the at leastone server, wherein at least one of the at least one server may comprise a local serverand / or a cloud server.Moreover, the method may comprise performing any of the steps of the method accordingto any of the preceding embodiments in an integrated intensive care unit system.Additionally and alternatively, the at least one successful weaning probability level maycomprise at least one successful weaning probability level threshold comprising at leastone successful weaning probability score defining the at least one successful weaningprobability level asScore Successful Weaning Probability LevelN.A. N.A. (-1)Score<Threshold / 2 Low (0)Score >= Threshold / 2 and Score<Threshold Medium (1)Score >= Threshold High (2)Furthermore, the at least one necessity probability level may comprise at least onenecessity probability level threshold comprising at least one necessity probability scoredefining the at least one necessity probability level asScore Necessity Probability LevelN.A. N.A. (-1)Score<Threshold / 2 Low (0)Score >= Threshold / 2 and Score<Threshold Medium (1)Score >= Threshold High (2)In one embodiment, the method may comprise performing any of the steps of the methodaccording to any of the preceding method embodiments by using at least one of the: firstmodule, second module, third module, fourth module, fifth module, sixth module, seventhmodule and eighth module.Moreover, the method may comprise diagnosing a probability of successful weaning frominvasive mechanical ventilation. The method may also comprise diagnosing a necessity forinitiating invasive mechanical ventilation. Furthermore, the retrieving step may comprise retrieving at least one data of the training of the at least one artificial-intelligence-assisted module. The at least one artificial- intelligence-assisted module may be trained using the at least one preprocessed data.Additionally and alternatively, the retrieving step may comprise retrieving at least oneventilator-measured input parameter, wherein the at least one ventilator-measured input may comprise at least one of, tidal volume, positive end-expiratory pressure (PEEP), peak inspiratory pressure, respiratory rate, inspiratory flow and / or waveform, and / or plateaupressure. The categorizing step may also comprise generating at least one respiratorymechanics metric, comprised in the at least one invasive mechanical status data. Thecategorizing step may further comprise generating at least one respiratory mechanicsmetrics according to at least one of the at least one ventilator-measured input, whereinthe at least one ventilator-measured input may comprise at least one of, driving pressure,airway resistance and / or mechanical power delivered to a respiratory system. The drivingpressure may be related to, for example the difference between the plateau pressure andPEEP. Airway resistance may be related to, for example the inspiratory flow waveform andthe difference between peak inspiratory pressure and plateau pressure.Further, the method may comprise activating at least one operating mode, wherein the atleast one operating mode may comprise generating at least one ventilation metricaccording to at least one respiratory mechanics metric. The method thus enables multiplemodes to be activated in parallel. The method may also comprise restricting the retrievingstep and / or categorizing step to the required at least one respiratory mechanics metric and / or the at least one data used to generate the at least one respiratory mechanics metric,for the at least one activated operating mode to be working properly. For example, theretrieving step would retrieve at least one data from only specific sensor(s), allowing more efficient use of computational power and less analysis / storage of unneeded data.Moreover, the method may comprise activating a volume-controlled ventilation operatingmode, wherein the volume-controlled ventilation operating mode may comprise generatingat least one mechanical power metric, wherein the at least one mechanical power metricmay comprise a metric of energy per unit time, for example Joules per minute, deliveredby a ventilator. The volume-controlled ventilation operating mode may also comprisegenerating at least one mechanical power metric via at least one of, tidal volume, positive end-expiratory pressure (PEEP) and / or plateau pressure, respiratory rate, and / or inspiratory flow and / or waveform.Furthermore, the method may comprise activating a pressure-support ventilation operatingmode, wherein the pressure-support ventilation operating mode may comprise generatingat least one predicted muscular pressure (Pmus) metric and / or transpulmonary pressuremetric. This operating mode enables the estimation of patient effort and lung stress, whichis a further advantage of the present invention. The pressure-support ventilation operatingmode may also comprise generating at least one predicted muscular pressure (Pmus)metric and / or transpulmonary pressure metric via at least one of ventilator parameter dataand / or at least one physiological data such as but not limited to data related tomeasurements via an esophageal ballon. Another example, would be data related toesophageal pressure, which would improve estimation of transpulmonary pressures or lung stress, showcasing an additional advantage of the present invention.Additionally, the method may comprise activating a pressure-control ventilation operatingmode, wherein the pressure-control ventilation operating mode may comprise generatingat least one quantitative metric of respiratory mechanics and stress, wherein the at leastone quantitative metric of respiratory mechanics and stress may comprise at least one ofdynamic lung compliance metric, driving pressure metric, derived indices of mechanicalpower, and / or derived indices of transpulmonary pressure. This operating mode mayenable assessment of ventilatory load and protective ventilation strategies, which is a further advantage of the present invention. The pressure-control ventilation operatingmode may also comprise generating at least one quantitative metric of respiratorymechanics and stress via at least one delivered tidal volume, positive end-expiratorypressure (PEEP), and / or respiratory rate. Compliance may be related to, for example, thetidal volume divided by the difference between plateau (or peak) pressure and PEEP, which may be adjusted for spontaneous effort.Further, the method may comprise displaying at least one component of any of themechanical power delivered to a respiratory system and / or the at least one mechanicalpower metric. The method may also comprise displaying at least one component of any ofthe mechanical power delivered to a respiratory system, such as elastic, resistive, PEEP related power, and / or the at least one mechanical power metric, as a percentage contribution to total power. Moreover, the at least one respiratory mechanics metric and / or the at least one ventilationmetric may be classified in at least one zone wherein further the at least one zone may bedelimited according to at least one range of values, wherein the at least one range ofvalues, wherein the at least one range of values may be defined, with respect to astandardized database, to assess safety and / or risk levels. The method may also comprise displaying the at least one respiratory mechanics metric and / or the at least one ventilationmetric. The method may further comprise displaying the at least one respiratory mechanicsmetric and / or the at least one ventilation metric with respect to the at least one zone itmay be classified in. The method may further comprise outputting at least one signalrelated to the at least one respiratory mechanics metric and / or the at least one ventilationmetric with respect to the at least one zone it is classified in.Additionally, the method may comprise simulating at least one ventilator simulationaccording to at least one ventilator parameter data, wherein simulating the at least oneventilator simulation may comprise simulating the at least one ventilator when at least oneventilator parameter data may be altered. This feature may allow clinicians to predict andprepare for how change would affect metrics such as but not limited to driving pressure, mechanical power, compliance.The method may comprise storing the at least one respiratory mechanics metric and / orthe at least one ventilation metric. The method may also comprise exporting the at leastone respiratory mechanics metric and / or the at least one ventilation metric. The methodmay further comprise integrating the at least one respiratory mechanics metric and / or theat least one ventilation metric into electronic health records.In another regard, the invention may also relate to an assessment method for conductinga spontaneous breathing test, the assessment method comprising retrieving at least onedata from at least one data source, preprocessing the at least one data to automaticallygenerate at least one preprocessed data, and analyzing the at least one preprocessed datawith respect to a standardized database.In one embodiment, the preprocessing step may comprise performing at least onemeasurement unit conversion, discarding at least one out-of-range value, processing at least one ventilator parameter data, processing at least one blood chemistry data, and / or processing at least one physiological data.Furthermore, the at least one preprocessed data may be categorized in at least one zonewherein further the at least one zone may be delimited according to at least one range of values, wherein the at least one range of values is defined, with respect to a standardized database, to assess readiness for a Spontaneous Breathing Trial. The at least one preprocessed data may be classified in at least one zone wherein further the at least one zone may be delimited according to at least one range of values, wherein the at least one range of values may be defined, with respect to a standardized database, to assess if the Spontaneous Breathing Trial is successful. The assessment method may further comprise displaying the result of the comparison step. In another embodiment, the retrieving step may comprise retrieving at least one respiratory rate data, at least one oxygen saturation (SpO2) data or at least one partial pressure of oxygen (PaO2) data, at least one heart rate data, at least one arrhythmia symptom status data or at least one myocardial symptom status data, at least one hypertensive urgency status data, and at least one clinical symptom of respiratory distress status data, wherein the analysis result may comprise a spontaneous breathing trial output.In a further regard, the invention relates to a compound method, wherein the compoundmethod comprises the method according to the method described herein and theassessment method according to the assessment method described herein.In a second aspect, the invention relates to a system for monitoring invasive mechanicalventilation, the system comprising a retrieving component configured to retrieve at least one data from at least one data source, a processing component configured to at leastpreprocess the at least one data to automatically generate at least one preprocessed data,a categorizing component configured to categorize the at least one preprocessed data into at least one invasive mechanical ventilation status data, and an activating component configured to activate at least one artificial-intelligence-assisted module, wherein the activating component may be configured to activate the at least one artificial-intelligence- assisted module based on the at least one invasive mechanical ventilation status data. The at least one invasive mechanical ventilation status data references the status of the invasive mechanical ventilation system. In the processing component may be configured to perform at least one measurement unit conversion, discard at least one out-of-range value, process at least one ventilator parameter data, process at least one blood chemistry data, and / or process at least one physiological data. Moreover, the at least one artificial-intelligence assisted module may comprise at least one of: a first module, a second module, a third module, a fourth module, a fifth module, a sixth module, a seventh module, and an eighth module. In one embodiment, the categorizing component may be configured to perform a categorizing step according to the at least one ventilator parameter data. Further, the system may be configured to determine which of the at least one artificial- intelligence-assisted module to activate. The system may further be configured to determine which at least one artificial-intelligence-assisted module to activate before prompting the activating component to activate the at least one artificial-intelligence- assisted module. Moreover, the system may be configured to activate a plurality of artificial-intelligence- assisted modules. The system may also be configured to determine which combination of the at least one artificial-intelligence-assisted modules to activate. The system may further be configured to determine which at least one artificial-intelligence-assisted module to activate, before prompting the activating component to activate the combination of the at least one artificial-intelligence-assisted modules. In one embodiment, the system may beconfigured to determine which at least one artificial-intelligence-assisted module toactivate may be depending on the at least one invasive mechanical ventilation status data. In one embodiment, the system may be configured to activate any combination of the first module, the second module, the third module and the fourth module, when the least one invasive mechanical ventilation status data comprises data indicating an active invasive mechanical ventilation. In another embodiment, the system may be configured to activate any combination of the fifth module, the sixth module, the seventh module and the eighth module, when the least one invasive mechanical ventilation status data comprises data indicating an inactive invasive mechanical ventilation. Furthermore, the system may be configured to predict a future invasive mechanical ventilation weaning trajectory by means of the at least one artificial-intelligence-assisted module, and / or predict a future invasive mechanical ventilation necessity trajectory by means of the at least one artificial-intelligence-assisted module. The system may also be configured to predict at least one probability of successful weaning from invasive mechanical ventilation and / or predict at least one probability of initiating invasive mechanical ventilation, successful weaning from mechanical ventilation may be defined as consecutive 48-hours without mechanical ventilation. In another embodiment, the system may be configured to predict at least one number of hours before successful weaning from mechanical ventilation at the current time and / or at least one number of hours before initiating mechanical ventilation at the current time. The system may also be configured to predict based on the at least one invasive mechanical ventilation status data. The system may further be configured to predict by means of the at least one artificial-intelligence assisted module. The system may additionally and alternatively be configured to predict based on the at least one preprocessed data. In a further embodiment, the first module may be configured to predict at least one probability of successful weaning from mechanical ventilation within 24 hours at the current time. The second module may be configured to predict at least one probability of successful weaning from mechanical ventilation within 48 hours at the current time. The third module may be configured to predict at least one probability of successful weaning from mechanical ventilation within 72 hours at the current time. The fourth module may be configured topredict at least one number of hours before successful weaning from mechanical ventilationat the current time. The fifth module may be configured to predict at least one probability of initiating mechanical ventilation within 24 hours at the current time. The sixth module may be configured to predict at least one probability of initiating mechanical ventilation within 48 hours at the current time. The seventh module may be configured to predict at least one probability of initiating mechanical ventilation within 72 hours at the current time.The eighth module may be configured to predict at least one number of hours beforeinitiating mechanical ventilation at the current time. The predicting steps may be performed hourly. Further, the system may be configured to automatically generate at least one successful weaning probability level, wherein the at least one successful weaning probability level expresses a lack of necessity for a patient’s invasive mechanical ventilation, wherein the successful weaning probability level may be based on the predicting of a future invasive mechanical ventilation weaning trajectory. The system may also be configured to automatically generate at least one risk level, wherein the at least one risk level expresses a necessity for a patient’s invasive mechanical ventilation, wherein the risk level may be based on the predicting of a future invasive mechanical ventilation necessity trajectory. In another embodiment, the system may be configured to output at least one decision- supporting-data based on the predicting of the invasive mechanical ventilation weaning trajectory. The system may be configured to output at least one decision-supporting-data based on the predicting of the invasive mechanical ventilation necessity trajectory. Moreover, the system may be configured to categorize at least one preprocessed data in at least one zone wherein further the at least one zone may be delimited according to at least one range of values, wherein the at least one range of values may be defined, with respect to a standardized database, to assess the necessity for invasive mechanical ventilation and / or to assess readiness for a Spontaneous Breathing Trial.Additionally, the system may be configured to categorize at least one preprocessing datain at least one zone wherein further the at least one zone may be delimited according toat least one range of values, wherein the at least one range of values may be defined, withrespect to a standardized database, to assess if a Spontaneous Breathing Trial is successful.The system may also be configured to categorize at least one preprocessing data in at leastone zone wherein further the at least one zone may be delimited according to at least onerange of values, wherein the at least one range of values may be defined, with respect toa standardized database, to assess readiness for extubation. The system may further beconfigured to categorize at least one preprocessing data in at least one zone whereinfurther the at least one zone may be delimited according to at least one range of values,wherein the at least one range of values, wherein the at least one range of values may bedefined, with respect to a standardized database, to assess readiness for weaning frommechanical ventilation. The system may be configured to output at least one signal relatedto the at least one preprocessed data with respect to the at least one zone it may beclassified in. The system may further be configured to output at least one signal related tothe at least one preprocessed data with respect to the at least one zone it may be classifiedin, such as but not limited to activating an alert according to the urgency of the at least one zone and / or the combination of zones. The alert may also relate to the combination of a successful Spontaneous Breathing Trial and readiness extubation. The alert may be related to at least one metric exceeding predefined clinical safety thresholds, such as but not limited to threshold for driving pressure, and / or mechanical power, wherein the at least one metric may be associated with risk of ventilator induced lung injury (VILI). Furthermore, the system may be configured to display the at least one preprocessed data with respect to the at least one zone it may be categorized in. The system may also be configured to display at least one successful weaning probability level and / or at least one risk level in function of time. In one embodiment, the system may be configured to display at least one input submitted by an authorized user corresponding the at least one successful weaning probability level and / or the at least one risk level at at least one time. The system may be configured to store at least one input submitted by an authorized user corresponding to the at least one successful weaning probability level and / or the at least one risk level at at least one time. That input may comprise preferably a note, a message… that may be related to the data or the time the input is corresponding to. In another embodiment, the system may be configured to prompt at least one authorizeduser to input at least one data point, wherein the at least one data point may comprisecomputer-readable input data points. The at least one data point may also comprise at least one selectable data point comprising at least one predetermined select data point, wherein the method comprises prompting at least one authorized user to select at least one option. The system may be configured to bidirectionally communicate with at least one server. For example, a user-interface may be implemented as a web-based application and / or progressive web app accessible via at least one end-user device. Further, the system may be configured to utilize any data of any of the preceding embodiments to train at least one algorithm. The training may be performed on the at least one server, wherein at least one of the at least one server comprises a local server and / or a cloud server. Moreover, the system may be configured to perform any of the steps of the method according to the method described herein in an integrated intensive care unit system. Additionally and alternatively, the at least one successful weaning probability level may comprise at least one successful weaning probability level threshold comprising at least one successful weaning probability score defining the at least one successful weaning probability level as Score Successful Weaning Probability LevelN.A. N.A. (-1)Score<Threshold / 2 Low (0)Score >= Threshold / 2 and Score<Threshold Medium (1)Score >= Threshold High (2) Furthermore, the at least one necessity probability level may comprise at least one necessity probability level threshold comprising at least one necessity probability score defining the at least one necessity probability level asScore Necessity Probability LevelScore Necessity Probability LevelN.A. N.A. (-1)Score<Threshold / 2 Low (0)Score >= Threshold / 2 and Score<Threshold Medium (1)Score >= Threshold High (2) In one embodiment, the system may be configured to perform any of the steps of the method according to any of the preceding method embodiments by using at least one of the: first module, second module, third module, fourth module, fifth module, sixth module, seventh module and eighth module. Moreover, the system may be configured to diagnose a probability of successful weaning from invasive mechanical ventilation. The system may also be configured to diagnose a necessity for initiating invasive mechanical ventilation. Furthermore, the retrieving component is configured to retrieve at least one data of the training of the at least one artificial-intelligence-assisted module. The at least one artificial- intelligence-assisted module may be configured to be trained using the at least one preprocessed data.Additionally and alternatively, the retrieving component may be configured to retrieve atleast one ventilator-measured input parameter, wherein the at least one ventilator-measured input may comprise at least one of, tidal volume, positive end-expiratorypressure (PEEP), peak inspiratory pressure, respiratory rate, inspiratory flow and / orwaveform, and / or plateau pressure. The categorizing component may also be configuredto generate at least one respiratory mechanics metric, comprised in the at least oneinvasive mechanical status data. The categorizing component may further be configuredto generate at least one respiratory mechanics metrics according to at least one of the at least one ventilator-measured input, wherein the at least one ventilator-measured inputmay comprise at least one of, driving pressure, airway resistance and / or mechanical powerdelivered to a respiratory system. The driving pressure may be related to, for example thedifference between the plateau pressure and PEEP. Airway resistance may be related to, for example the inspiratory flow waveform and the difference between peak inspiratory pressure and plateau pressure.Further, the system may be configured to activate at least one operating mode, whereinthe at least one operating mode may be configured to generate at least one ventilationmetric according to at least one respiratory mechanics metric. The method thus enablesmultiple modes to be activated in parallel. The system may also be configured to restrictthe retrieving component and / or categorizing component to be configured to retrieve the required at least one respiratory mechanics metric and / or the at least one data used to generate the at least one respiratory mechanics metric, for the at least one activatedoperating mode to be working properly. For example, the retrieving component would beconfigured to retrieve at least one data from only specific sensor(s), allowing more efficient use of computational power and less analysis / storage of unneeded data.Moreover, the system may be configured to activate a volume-controlled ventilationoperating mode, wherein the volume-controlled ventilation operating mode may beconfigured to generate at least one mechanical power metric, wherein the at least onemechanical power metric may comprise a metric of energy per unit time, for example joulesper minutes, delivered by a ventilator. The volume-controlled ventilation operating modemay also be configured to generate at least one mechanical power metric via at least one of, tidal volume, positive end-expiratory pressure (PEEP) and / or plateau pressure, respiratory rate, and / or inspiratory flow and / or waveform.Furthermore, the system may be configured to activate a pressure-support ventilationoperating mode, wherein the pressure-support ventilation operating mode may beconfigured to generate at least one predicted muscular pressure (Pmus) metric and / ortranspulmonary pressure metric. This operating mode enables the estimation of patienteffort and lung stress, which is a further advantage of the present invention. The pressure-support ventilation operating mode may also be configured to generate at least onepredicted muscular pressure (Pmus) metric and / or transpulmonary pressure metric via at least one of ventilator parameter data and / or at least one physiological data, such as but not limited to data related to measurements via an esophageal ballon. Another example, would be data related to esophageal pressure, which would improve estimation of transpulmonary pressures or lung stress, showcasing an additional advantage of the present invention.Additionally, the system may be configured to activate a pressure-control ventilationoperating mode, wherein the pressure-control ventilation operating mode may beconfigured to generate at least one quantitative metric of respiratory mechanics and stress, wherein the at least one quantitative metric of respiratory mechanics and stress maycomprise at least one of dynamic lung compliance metric, driving pressure metric, derivedindices of mechanical power, and / or derived indices of transpulmonary pressure. Thisoperating mode may enable assessment of ventilatory load and protective ventilationstrategies, which is a further advantage of the present invention. The pressure-controlventilation operating mode may also be configured to generate at least one quantitativemetric of respiratory mechanics and stress via at least one delivered tidal volume, positiveend-expiratory pressure (PEEP), and / or respiratory rate. Compliance may be related to,for example, the tidal volume divided by the difference between plateau (or peak) pressure and PEEP, which may be adjusted for spontaneous effort.Further, the system may be configured to display at least one component of any of themechanical power delivered to a respiratory system and / or the at least one mechanicalpower metric. The system may also be configured to display at least one component of anyof the mechanical power delivered to a respiratory system such as elastic, resistive, PEEPrelated power, and / or the at least one mechanical power metric, as a percentagecontribution to total power.Moreover, the system may be configured to classify the at least one respiratory mechanicsmetric and / or the at least one ventilation metric in at least one zone wherein further theat least one zone may be delimited according to at least one range of values, wherein theat least one range of values, wherein the at least one range of values may be defined, withrespect to a standardized database, to assess safety and / or risk levels. The system mayalso be configured to display the at least one respiratory mechanics metric and / or the atleast one ventilation metric. The system may further be configured to display the at leastone respiratory mechanics metric and / or the at least one ventilation metric with respect tothe at least one zone it may be classified in. The system may further be configured tooutput at least one signal related to the at least one respiratory mechanics metric and / orthe at least one ventilation metric with respect to the at least one zone it may be classifiedin.Additionally, the system may be configured to simulate at least one ventilator simulationaccording to at least one ventilator parameter data. The system may also be configured tosimulate the at least one ventilator when at least one ventilator parameter data may bealtered. This feature may allow clinicians to predict and prepare for how change wouldaffect metrics such as but not limited to driving pressure, mechanical power, compliance.The system may be configured to store the at least one respiratory mechanics metricand / or the at least one ventilation metric. The system may also be configured to exportthe at least one respiratory mechanics metric and / or the at least one ventilation metric.The system may further be configured to integrate the at least one respiratory mechanicsmetric and / or the at least one ventilation metric into electronic health records. In another regard, the invention relates to a system for conducting a spontaneous breathing test, the system comprising a retrieving component configured to retrieve at least one data from at least one data source, a processing component configured to preprocess the at least one data to automatically generate at least one preprocessed data,a comparing component configured to compare the at least one preprocessed data with astandardized database. In one embodiment, the processing component may be configured to perform at least one measurement unit conversion, discard at least one out-of-range value, process at least one ventilator parameter data, process at least one blood chemistry data, and / or process at least one physiological data. Furthermore, the system may be configured to categorize the at least one preprocesseddata in at least one zone wherein further the at least one zone may be delimited accordingto at least one range of values, wherein the at least one range of values may be defined, with respect to a standardized database, to assess readiness for a Spontaneous Breathing Trial. The system may be configured to categorize the at least one preprocessed data in at leastone zone wherein further the at least one zone may be delimited according to at least onerange of values, wherein the at least one range of values may be defined, with respect toa standardized database, to assess if the Spontaneous Breathing Trial is successful.The system the system may comprise a display component, wherein the display component may be configured to display the result of the comparing component. In another embodiment, the retrieving component may be configured to retrieve at least one respiratory rate data, at least one oxygen saturation (SpO2) data or at least one partial pressure of oxygen (PaO2) data, at least one heart rate data, at least one arrhythmia symptom status data or at least one myocardial symptom status data, at least one hypertensive urgency status data, and at least one clinical symptom of respiratory distress status data wherein the comparing component may be configured to output a comparisonresult wherein the comparison result comprises a spontaneous breathing trial output.In a further regard, the invention relates to a system, wherein the system comprises any combination of, the systems previously described herein. In one embodiment, the method may comprise utilizing the system according to any of the preceding system embodiments to carry out the method according to any of the preceding method embodiments. The method may also comprise utilizing components of the system according to any of the preceding system embodiments to carry out given steps of the method according to any of the preceding method embodiments. In another embodiment, the assessment method may comprise utilizing the system according to any of the preceding system embodiments to carry out the assessmentmethod according to any of the preceding assessment method embodiments. Theassessment method may also comprise utilizing components of the system according to any of the preceding system embodiments to carry out given steps of the assessment method according to any of the preceding assessment method embodiments. In a further embodiment, the compound method may comprise utilizing the system according to any of the preceding system embodiments to carry out the compound method according to any of the preceding compound method embodiments. The compound methodmay also comprise utilizing components of the system according to any of the precedingsystem embodiments to carry out given steps of the compound method according to any of the preceding compound method embodiments.In a third aspect, the invention relates to a computer program comprising instructionswhich, when the program may be executed by a computer, cause the computer to carryout the method according to the method described herein, according to the assessment method described herein, and / or according to the compound method as described herein. In a fourth aspect, the invention relates to a non-transient computer-readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the method according to the method described herein, according to the assessment method described herein, and / or according to the compound method as described herein. In a fifth aspect, the invention relates to the use of the system according to the system described herein. The invention relates further to the use of the system for carrying out the method according to the method described herein, according to the assessment method described herein, and / or according to the compound method as described herein. Furthermore, the method can be performed for the assembling, testing and / or calibrating the system without the presence of a patient. Moreover, the training of the at least one AI module can be performed without the presence of a patient. The present technology is also described by the following numbered embodiments. Below, method embodiments will be discussed. These embodiments are abbreviated by the letter “M” followed by a number. When reference is herein made to a method embodiment, those embodiments are meant. The present technology is also described by the following numbered embodiments. Below, method embodiments will be discussed. These embodiments are abbreviated by the letter “M” followed by a number. When reference is herein made to a method embodiment, those embodiments are meant.M1. A method for monitoring invasive mechanical ventilation, the method comprisingretrieving at least one data from at least one data source, preprocessing the at least one data to automatically generate at least one preprocessed data, categorizing the at least one preprocessed data into at least one invasivemechanical ventilation status data, andactivating at least one artificial-intelligence-assisted module,wherein the at least one artificial-intelligence-assisted module is activatedaccording to the at least one invasive mechanical ventilation status data.M2. The method according to the preceding embodiment, wherein the preprocessingstep comprises performing at least one measurement unit conversion.M3. The method according to any of the preceding embodiments, wherein thepreprocessing step comprises discarding at least one out-of-range value.M4. The method according to any of the preceding embodiments, wherein thepreprocessing step comprises processing at least one ventilator parameter data.M5. The method according to any of the preceding embodiments, wherein thepreprocessing step comprises processing at least one blood chemistry data.M6. The method according to any of the preceding embodiments, wherein thepreprocessing step comprises processing at least one physiological data.M7. The method according to any of the preceding embodiments, wherein the at leastone artificial-intelligence assisted module comprises at least one of: afirst module, a second module, a third module, a fourth module,a fifth module, a sixth module, a seventh module, and an eighth module.M8. The method according to any of the preceding embodiments with the features ofembodiment M4, wherein the categorizing step comprises performing the categorizing step according to the at least one ventilator parameter data.M9. The method according to any of the preceding embodiments, wherein the methodcomprises determining which of the at least one artificial-intelligence-assisted module to activate.M10. The method according to any of the three preceding embodiments, wherein thedetermining step of which at least one artificial-intelligence-assisted module toactivate, precedes the step of activating the at least one artificial-intelligence-assisted module.M11. The method according to any of the preceding embodiments, wherein the methodcomprises activating a plurality of artificial-intelligence-assisted modules.M12. The method according to the preceding embodiment, wherein the methodcomprises determining which combination of the at least one artificial-intelligence-assisted modules to activate.M13. The method according to the preceding embodiment, wherein the determiningstep of which at least one artificial-intelligence-assisted module to activate, precedes the step of activating the combination of the at least one artificial-intelligence-assisted modules.M14. The method according to any of the preceding embodiments with features of anyof embodiments M9-M13, wherein determining which at least one artificial- intelligence-assisted module to activate depends on the at least one invasivemechanical ventilation status data.M15. The method according to embodiments M11 and with the features of embodimentM7, wherein any combination of the first module, the second module, the third module and the fourth module can be activated when the least one invasivemechanical ventilation status data comprises data indicating an active invasivemechanical ventilation.M16. The method according to embodiment M11 and with the features of embodimentM7, wherein any combination of the fifth module, the sixth module, the seventhmodule and the eighth module can be activated when the least one invasivemechanical ventilation status data comprises data indicating an inactive invasivemechanical ventilation.M17. The method according to any of the preceding embodiments, wherein the at leastone artificial-intelligence-assisted module comprises predicting a future invasive mechanical ventilation weaning trajectory.M18. The method according to any of the preceding embodiments, wherein the at leastone artificial-intelligence-assisted module comprises predicting a future invasivemechanical ventilation necessity trajectory.M19. The method according to any of the preceding embodiments, wherein the methodcomprises predicting at least one probability of successful weaning from invasive mechanical ventilation.M20. The method according to any of the preceding embodiments, wherein the methodcomprises predicting at least one probability of initiating invasive mechanical ventilation.M21. The method according to embodiment M19 wherein successful weaning frommechanical ventilation is defined as consecutive 48-hours without mechanical ventilation.M22. The method according to any of the preceding embodiments, wherein the methodcomprises predicting at least one number of hours before successful weaning from mechanical ventilation at the current time.M23. The method according to any of the preceding embodiments, wherein the methodcomprises predicting at least one number of hours before initiating mechanical ventilation at the current time.M24. The method according to any preceding embodiments with the features of any ofembodiments M17-M23, wherein the predicting is based on the at least oneinvasive mechanical ventilation status data.M25. The method according to any preceding embodiments with the features of any ofembodiments M17-M24, wherein the predicting is performed by the at least one artificial-intelligence assisted module.M26. The method according to any preceding embodiments with the features of any ofembodiments M17-M25 wherein the predicting is based on the at least onepreprocessed data.M27. The method according to any of the preceding embodiments with features ofembodiment M7, wherein the first module comprises predicting at least one probability of successful weaning from mechanical ventilation within 24 hours at the current time.M28. The method according to any of the preceding embodiments with features ofembodiment M7, wherein the second module comprises predicting at least one probability of successful weaning from mechanical ventilation within 48 hours at the current time.M29. The method according to any of the preceding embodiments with features ofembodiment M7, wherein the third module comprises predicting at least oneprobability of successful weaning from mechanical ventilation within 72 hours at the current time.M30. The method according to any of the preceding embodiments with features ofembodiment M7, wherein the fourth module comprises predicting at least onenumber of hours before successful weaning from mechanical ventilation at the current time.M31. The method according to any of the preceding embodiments with features ofembodiment M7, wherein the fifth module comprises predicting at least oneprobability of initiating mechanical ventilation within 24 hours at the current time.M32. The method according to any of the preceding embodiments with features ofembodiment M7, wherein the sixth module comprises predicting at least oneprobability of initiating mechanical ventilation within 48 hours at the current time.M33. The method according to any of the preceding embodiments with features ofembodiment M7, wherein the seventh module comprises predicting at least oneprobability of initiating mechanical ventilation within 72 hours at the current time.M34. The method according to any of the preceding embodiments with features ofembodiment M7, wherein the eighth module comprises predicting at least onenumber of hours before initiating mechanical ventilation at the current time.M35. The method according to any of the preceding embodiments with the features ofany of embodiments M17-M34, wherein the predicting steps is performed hourly.M36. The method according to any of the preceding embodiments, wherein the methodcomprises automatically generating at least one successful weaning probability level, wherein the at least one successful weaning probability level expresses a lack of necessity for a patient’s invasive mechanical ventilation.M37. The method according to the preceding embodiment, wherein the successfulweaning probability level is based on the predicting of a future invasive mechanicalventilation weaning trajectory.M38. The method according to any of the preceding embodiments, wherein the methodcomprises automatically generating at least one risk level, wherein the at leastone risk level expresses a necessity for a patient’s invasive mechanical ventilation.M39. The method according to the preceding embodiment, wherein the risk level isbased on the predicting of a future invasive mechanical ventilation necessity trajectory.M40. The method according to any of the preceding embodiments and with the featuresof embodiment M17, wherein the method comprises outputting at least one decision-supporting-data based on the predicting of the invasive mechanical ventilation weaning trajectory.M41. The method according to any of the preceding embodiments and with the featuresof embodiment M18, wherein the method comprises outputting at least one decision-supporting-data based on the predicting of the invasive mechanical ventilation necessity trajectory.M42. The method according to any of the preceding method embodiments, wherein theat least one preprocessed data is classified in at least one zone wherein furtherthe at least one zone is delimited according to at least one range of values, wherein the at least one range of values is defined, with respect to a standardized database, to assess the necessity for invasive mechanical ventilation.M43. The method according to any of the preceding method embodiments, wherein theat least one preprocessed data is classified in at least one zone wherein further the at least one zone is delimited according to at least one range of values, wherein the at least one range of values is defined, with respect to a standardized database, to assess readiness for a Spontaneous Breathing Trial.M44. The method according to any of the preceding method embodiments, wherein theat least one preprocessing data is classified in at least one zone wherein further the at least one zone is delimited according to at least one range of values, wherein the at least one range of values is defined, with respect to a standardized database, to assess if a Spontaneous Breathing Trial is successful.M45. The method according to any of the preceding method embodiments, wherein theat least one preprocessing data is classified in at least one zone wherein further the at least one zone is delimited according to at least one range of values, wherein the at least one range of values is defined, with respect to a standardizeddatabase, to assess readiness for extubation.M46. The method according to any of the preceding method embodiments, wherein theat least one preprocessing data is classified in at least one zone wherein further the at least one zone is delimited according to at least one range of values, wherein the at least one range of values, wherein the at least one range of values is defined, with respect to a standardized database, to assess readiness for weaning from mechanical ventilation.M47. The method according to any of the two preceding method embodiments, whereinthe method comprises displaying the at least one preprocessed data with respect to the at least one zone it is classified in.M48. The method according to any of the preceding method embodiments, wherein themethod comprises outputting at least one signal related to the at least onepreprocessed data with respect to the at least one zone it is classified in.M49. The method according to any of the preceding method embodiments with thefeatures according to embodiment M36 wherein the method comprises displayingat least one successful weaning probability level in function of time.M50. The method according to any of the preceding method embodiments with thefeatures according to embodiment M38 wherein the method comprises displayingat least one risk level in function of time.M51. The method according to any of the preceding embodiments with the featuresaccording to any of embodiments M36 and M38 wherein the method comprisesstoring at least one input submitted by an authorized user corresponding to the atleast one successful weaning probability level and / or the at least one risk level atat least one time.M52. The method according to any of the preceding embodiments with the featuresaccording to any of embodiments M36 and M38 wherein the method comprisesdisplaying at least one input submitted by an authorized user corresponding the at least one successful weaning probability level and / or the at least one risk level at at least one time.M53. The method according to any of the preceding embodiments, wherein the methodcomprises prompting at least one authorized user to input at least one data point.M54. The method according to the preceding embodiment, wherein the at least one datapoint comprises computer-readable input data points.M55. The method according to any of the two preceding embodiments, wherein the atleast one data point comprises at least one selectable data point comprising at least one predetermined select data point, wherein the method comprises prompting at least one authorized user to select at least one option.M56. The method according to any of the preceding embodiments, wherein the methodcomprises bidirectionally communicating with at least one server.M57. The method according to any of the preceding embodiments, wherein the methodcomprises utilizing any data of any of the preceding embodiments to train at least one algorithm.M58. The method according to the two preceding embodiments, wherein the trainingstep is performed on the at least one server.M59. The method according to any of the preceding embodiment, wherein the methodcomprises performing any of the steps of the method according to any of the preceding embodiments in an integrated intensive care unit system.M60. The method according to any of the preceding embodiments and with the featuresof embodiment M56, wherein at least one of the at least one server comprises a local server.M61. The method according to any of the preceding embodiments and with the featuresof embodiment M56, wherein at least one of the at least one server comprises a cloud server.M62. The method according to any of the preceding embodiments and with the featuresof embodiments M36, wherein the at least one successful weaning probability levelcomprises at least one successful weaning probability level threshold comprisingat least one successful weaning probability score defining the at least onesuccessful weaning probability level asScore Successful Weaning Probability LevelN.A. N.A. (-1)Score<Threshold / 2 Low (0)Score >= Threshold / 2 and Score<Threshold Medium (1)Score >= Threshold High (2)M63. The method according to any of the preceding embodiments and with the featuresof embodiments M38, wherein the at least one necessity probability levelcomprises at least one necessity probability level threshold comprising at least onenecessity probability score defining the at least one necessity probability level asScore Necessity Probability LevelN.A. N.A. (-1)Score<Threshold / 2 Low (0)Score >= Threshold / 2 and Score<Threshold Medium (1)Score >= Threshold High (2)M64. The method according to any of the preceding method embodiments and with thefeatures of embodiment M7, wherein the method comprises performing any of the steps of the method according to any of the preceding method embodiments by using at least one of the: first module, second module, third module, fourth module,fifth module, sixth module, seventh module and eighth module.M65. The method according to any of the previous method embodiments with thefeatures of embodiments M19, wherein the method comprises diagnosing a probability of successful weaning from invasive mechanical ventilation.M66. The method according to any of the previous method embodiments with thefeatures of embodiments M20, wherein the method comprises diagnosing a necessity for initiating invasive mechanical ventilation.M67. The method according to any of the preceding method embodiments wherein theretrieving step comprises retrieving at least one data of the training of the at least one artificial-intelligence-assisted module.M68. The method according to any of the preceding method embodiments wherein theat least one artificial-intelligence-assisted module is trained using the at least onepreprocessed data.M69. The method according to any of the preceding method embodiments, wherein theretrieving step comprises retrieving at least one ventilator-measured input parameter.M70. The method according to any of the preceding embodiments with the features ofembodiment M69, wherein the at least one ventilator-measured input comprises at least one of, tidal volume, positive end-expiratory pressure (PEEP), peak inspiratory pressure, respiratory rate, inspiratory flow and / or waveform, and / or plateau pressure.M71. The method according to any of the preceding method embodiments, wherein thecategorizing step comprises generating at least one respiratory mechanics metric, comprised in the at least one invasive mechanical status data.M72. The method according to any of the preceding embodiments with the features ofembodiment M70, wherein the categorizing step comprises generating at least onerespiratory mechanics metrics according to at least one of the at least oneventilator-measured input.M73. The method according to any of the preceding embodiments with the features ofembodiment M71, wherein the at least one ventilator-measured input comprises at least one of, driving pressure, airway resistance and / or mechanical power delivered to a respiratory system.M74. The method according to any of the preceding method embodiments, with thefeatures of M71, wherein the method comprises activating at least one operatingmode, wherein the at least one operating mode comprises generating at least oneventilation metric according to at least one respiratory mechanics metric.M75. The method according to any of the preceding method embodiments, with thefeatures of M74, wherein the method comprises restricting the retrieving step and / or categorizing step to the required at least one respiratory mechanics metricand / or the at least one data used to generate the at least one respiratory mechanics metric, for the at least one activated operating mode to be workingproperly.M76. The method according to any of the preceding method embodiments, with thefeatures of M74, wherein the method comprises activating a volume-controlled ventilation operating mode, wherein the volume-controlled ventilation operating mode comprises generating at least one mechanical power metric, wherein the at least one mechanical power metric comprises a metric of energy per unit time delivered by a ventilator.M77. The method according to any of the preceding method embodiments, with thefeatures of M70 and M76, wherein the volume-controlled ventilation operatingmode comprises generating at least one mechanical power metric via at least one of, tidal volume, positive end-expiratory pressure (PEEP) and / or plateau pressure, respiratory rate, and / or inspiratory flow and / or waveform.M78. The method according to any of the preceding method embodiments, with thefeatures of M74, wherein the method comprises activating a pressure-support ventilation operating mode, wherein the pressure-support ventilation operating mode comprises generating at least one predicted muscular pressure (Pmus) metric and / or transpulmonary pressure metric.M79. The method according to any of the preceding method embodiments, with thefeatures of M4 and / or M6, and M78, wherein the pressure-support ventilationoperating mode comprises generating at least one predicted muscular pressure (Pmus) metric and / or transpulmonary pressure metric via at least one of ventilatorparameter data and / or at least one physiological data.M80. The method according to any of the preceding method embodiments, with thefeatures of M74, wherein the method comprises activating a pressure-control ventilation operating mode, wherein the pressure-control ventilation operatingmode comprises generating at least one quantitative metric of respiratorymechanics and stress.M81. The method according to any of the preceding method embodiments, with thefeatures of M80, wherein at least one quantitative metric of respiratory mechanics and stress comprises at least one of dynamic lung compliance metric, driving pressure metric, derived indices of mechanical power, and / or derived indices of transpulmonary pressure.M82. The method according to any of the preceding method embodiments, with thefeatures of M70 and M80, wherein the pressure-control ventilation operating modecomprises generating at least one quantitative metric of respiratory mechanics and stress via at least one delivered tidal volume, positive end-expiratory pressure(PEEP), and / or respiratory rate.M83. The method according to any of the preceding embodiments, with the features ofembodiments M73 and / or M76, wherein the method comprises displaying at least one component of any of the mechanical power delivered to a respiratory system and / or the at least one mechanical power metric.M84. The method according to any of the preceding embodiments, with the features ofembodiments M73 and / or M76, wherein the method comprises displaying at leastone component of any of the mechanical power delivered to a respiratory system and / or the at least one mechanical power metric, as a percentage contribution to total power.M85. The method according to any of the preceding method embodiments, with thefeatures of embodiment M74, wherein the at least one respiratory mechanicsmetric and / or the at least one ventilation metric is classified in at least one zone wherein further the at least one zone is delimited according to at least one range of values, wherein the at least one range of values, wherein the at least one range of values is defined, with respect to a standardized database, to assess safety and / or risk levels.M86. The method according to any of the preceding method embodiments, with thefeatures of embodiment M74 wherein the method comprises displaying the at leastone respiratory mechanics metric and / or the at least one ventilation metric.M87. The method according to any of the preceding method embodiments, with thefeatures of embodiment M85 wherein the method comprises displaying the at leastone respiratory mechanics metric and / or the at least one ventilation metric with respect to the at least one zone it is classified in.M88. The method according to any of the preceding method embodiments, wherein themethod comprises outputting at least one signal related to the at least onerespiratory mechanics metric and / or the at least one ventilation metric withrespect to the at least one zone it is classified in.M89. The method according to any of the preceding method embodiments, wherein themethod comprises simulating at least one ventilator simulation according to at least one ventilator parameter data.M90. The method according to any of the preceding embodiments, with the features ofembodiment M89, wherein simulating the at least one ventilator simulation comprises simulating the at least one ventilator when at least one ventilator parameter data is altered.M91. The method according to any of the preceding embodiments, with the features of embodiment M74, wherein the method comprises storing the at least one respiratory mechanics metric and / or the at least one ventilation metric.M92. The method according to any of the preceding embodiments, with the features ofembodiment M74, wherein the method comprises exporting the at least one respiratory mechanics metric and / or the at least one ventilation metric.M93. The method according to any of the preceding embodiments, with the features ofembodiment M74, wherein the method comprises integrating the at least one respiratory mechanics metric and / or the at least one ventilation metric into electronic health records.Below, assessment method embodiments will be discussed. These embodiments areabbreviated with the letter “A” followed by a number. Whenever reference is herein made to assessment method embodiments, these embodiments are meant.A1. An assessment method for conducting a spontaneous breathing test, theassessment method comprisingretrieving at least one data from at least one data source,preprocessing the at least one data to automatically generate at least one preprocessed data, analysing the at least one preprocessed data with respect to a standardizeddatabase.A2. The assessment method according to any of the preceding assessment methodembodiments, wherein the preprocessing step comprises performing at least one measurement unit conversion.A3. The assessment method according to any of the preceding assessment methodembodiments, wherein the preprocessing step comprises discarding at least one out-of-range value.A4. The assessment method according to any of the preceding assessment methodembodiments, wherein the preprocessing step comprises processing at least one ventilator parameter data.A5. The assessment method according to any of the preceding assessment methodembodiments, wherein the preprocessing step comprises processing at least one blood chemistry data.A6. The assessment method according to any of the preceding assessment methodembodiments, wherein the preprocessing step comprises processing at least one physiological data.A7. The assessment method according to any of the preceding assessment methodembodiments, wherein the at least one preprocessed data is classified in at least one zone wherein further the at least one zone is delimited according to at least one range of values, wherein the at least one range of values is defined, with respect to a standardized database, to assess readiness for a Spontaneous Breathing Trial.A8. The assessment method according to any of the preceding assessment methodembodiments, wherein the at least one preprocessed data is classified in at least one zone wherein further the at least one zone is delimited according to at least one range of values, wherein the at least one range of values is defined, with respect to a standardized database, to assess if the Spontaneous Breathing Trial is successful.A9. The assessment method according to the preceding assessment methodembodiment, wherein the assessment method comprises further comprising displaying the result of the comparison step.A10. The assessment method according to any of the preceding assessment methodembodiments, wherein the retrieving step comprises retrieving at least one respiratory rate data.A11. The assessment method according to any of the preceding assessment method,wherein the retrieving step comprises retrieving at least one oxygen saturation (SpO2) data.A12. The assessment method according to any of the preceding assessmentembodiment, wherein the retrieving step comprises retrieving at least one partial pressure of oxygen (PaO2) data.A13. The assessment method according to any of the preceding assessment methodswherein the retrieving step comprises retrieving at least one heart rate data.A14. The assessment method according to any of the preceding assessment methodswherein the retrieving step comprises retrieving at least one arrhythmia symptom status data.A15. The assessment method according to any of the preceding assessment methodwherein the retrieving step comprises retrieving at least one myocardial symptom status data.A16. The assessment method according to any of the preceding assessment methodswherein the retrieving step comprises retrieving at least one hypertensive urgency status data.A17. The assessment method according to any of the preceding assessment methodembodiments wherein the retrieving step comprises retrieving at least one clinical symptom of respiratory distress status data.A18. The assessment method according to any of the preceding assessment methodswith the features of assessment method embodiments A10, A11 or A12, A13, A14or A15, A16, and A17 wherein the analysis result comprises a spontaneousbreathing trial output. Below, compound method embodiments will be discussed. These embodiments are abbreviated with the letter “B” followed by a number. Whenever reference is herein made to compound method embodiments, these embodiments are meant.B1. A compound method, wherein the compound method comprisesthe method according to any of the preceding method embodiments and the assessment method according to any of the preceding assessment methodembodiments.S1. A system for monitoring invasive mechanical ventilation, the system comprisinga retrieving component configured to retrieve at least one data from at least one data source, a processing component configured to at least preprocess the at least one data to automatically generate at least one preprocessed data,a categorizing component configured to categorize the at least one preprocesseddata into at least one invasive mechanical ventilation status data, and aactivating component configured to activate at least one artificial-intelligence-assisted module, wherein the activating component is configured to activate the at least oneartificial-intelligence-assisted module based on the at least one invasive mechanical ventilation status data.S2. The system according to the preceding embodiment, wherein the processingcomponent is configured to perform at least one measurement unit conversion.S3. The system according to any of the preceding system embodiments, wherein theprocessing component is configured to discard at least one out-of-range value.S4. The system according to any of the preceding system embodiments, wherein theprocessing component is configured to process at least one ventilator parameter data.S5. The system according to any of the preceding system embodiments, wherein theprocessing component is configured to process at least one blood chemistry data.S6. The system according to any of the preceding system embodiments, wherein theprocessing component is configured to process at least one physiological data.S7. The system according to any of the preceding system embodiments, wherein theat least one artificial-intelligence assisted module comprises at least one of: a first module, a second module, a third module, a fourth module, a fifth module, a sixth module, a seventh module, and an eighth module.S8. The system according to any of the preceding embodiments with the features ofembodiment S4, wherein the categorizing component is configured to perform acategorizing step according to the at least one ventilator parameter data.S9. The system according to any of the preceding system embodiments, wherein thesystem is configured to determine which of the at least one artificial-intelligence- assisted module to activate.S10. The system according to any of the preceding embodiments, wherein the systemis configured to determine which at least one artificial-intelligence-assisted module to activate before prompting the activating component to activate the at least oneartificial-intelligence-assisted module.S11. The system according to any of the preceding embodiments, wherein the systemis configured to activate a plurality of artificial-intelligence-assisted modules.S12. The system according to the preceding embodiments, wherein the system isconfigured to determine which combination of the at least one artificial- intelligence-assisted modules to activate.S13. The system according to the preceding embodiment, wherein the system isconfigured to determine which at least one artificial-intelligence-assisted module to activate, before prompting the activating component to activate thecombination of the at least one artificial-intelligence-assisted modules.S14. The system according to any of the preceding embodiments with the features ofany of embodiments S9-S13, wherein the system is configured to determine which at least one artificial-intelligence-assisted module to activate depending on the atleast one invasive mechanical ventilation status data.S15. The system according to embodiment S11, with the features of embodiment S7,wherein the system is configured to activate any combination of the first module,the second module, the third module and the fourth module, when the least one invasive mechanical ventilation status data comprises data indicating an active invasive mechanical ventilation.S16. The system according to embodiment S11, with the features of embodiment S7wherein the system is configured to activate any combination of the fifth module,the sixth module, the seventh module and the eighth module, when the least oneinvasive mechanical ventilation status data comprises data indicating an inactive invasive mechanical ventilation.S17. The system according to any of the preceding embodiments, wherein the systemis configured to predict a future invasive mechanical ventilation weaning trajectory by means of the at least one artificial-intelligence-assisted module.S18. The system according to any of the preceding embodiments, wherein the systemis configured to predict a future invasive mechanical ventilation necessity trajectory by means of the at least one artificial-intelligence-assisted module.S19. The system according to any of the preceding embodiments, wherein the systemis configured to predict at least one probability of successful weaning from invasive mechanical ventilation.S20. The system according to any of the preceding embodiments, wherein the systemis configured to predict at least one probability of initiating invasive mechanical ventilation.S21. The system according to embodiment S19, wherein successful weaning frommechanical ventilation is defined as consecutive 48-hours without mechanical ventilation.S22. The system according to any of the preceding embodiments, wherein the systemis configured to predict at least one number of hours before successful weaning from mechanical ventilation at the current time.S23. The system according to any of the preceding embodiments, wherein the systemis configured to predict at least one number of hours before initiating mechanical ventilation at the current time.S24. The system according to any preceding embodiments with the features of any ofembodiments S17-S23, wherein the system is configured to predict based on the at least one invasive mechanical ventilation status data.S25. The system according to any preceding embodiments with the features of any ofembodiments S17-S24, wherein the system is configured to predict by means of the at least one artificial-intelligence assisted module.S26. The system according to any preceding embodiments with the features of any ofembodiments S17-S25, wherein the system is configured to predict based on the at least one preprocessed data.S27. The system according to any of the preceding embodiments with features ofembodiment S7, wherein the first module is configured to predict at least one probability of successful weaning from mechanical ventilation within 24 hours at the current time.S28. The system according to any of the preceding embodiments with features ofembodiment S7, wherein the second module is configured to predict at least one probability of successful weaning from mechanical ventilation within 48 hours at the current time.S29. The system according to any of the preceding embodiments with features ofembodiment S7, wherein the third module is configured to predict at least one probability of successful weaning from mechanical ventilation within 72 hours atthe current time.S30. The system according to any of the preceding embodiments with features ofembodiment S7, wherein the fourth module is configured to predict at least one number of hours before successful weaning from mechanical ventilation at the current time.S31. The system according to any of the preceding embodiments with features ofembodiment S7, wherein the fifth module is configured to predict at least one probability of initiating mechanical ventilation within 24 hours at the current time.S32. The system according to any of the preceding embodiments with features ofembodiment S7, wherein the sixth module is configured to predict at least one probability of initiating mechanical ventilation within 48 hours at the current time.S33. The system according to any of the preceding embodiments with features ofembodiment S7, wherein the seventh module is configured to predict at least one probability of initiating mechanical ventilation within 72 hours at the current time.S34. The system according to any of the preceding embodiments with features ofembodiment S7, wherein the eighth module is configured to predict at least one number of hours before initiating mechanical ventilation at the current time.S35. The system according to any of the preceding embodiments with the features ofany of embodiments S17-S34, wherein the predicting steps is performed hourly.S36. The system according to any of the preceding embodiments, wherein the systemis configured to automatically generate at least one successful weaning probabilitylevel, wherein the at least one successful weaning probability level expresses a lack of necessity for a patient’s invasive mechanical ventilation.S37. The system according to the preceding embodiment, wherein the successfulweaning probability level is based on the predicting of a future invasive mechanical ventilation weaning trajectory.S38. The system according to any of the preceding embodiments, wherein the systemis configured to automatically generate at least one risk level, wherein the at least one risk level expresses a necessity for a patient’s invasive mechanical ventilation.S39. The system according to the preceding embodiment, wherein the risk level isbased on the predicting of a future invasive mechanical ventilation necessity trajectory.S40. The system according to any of the preceding embodiments and with the featuresof embodiment S17, wherein the system is configured to output at least one decision-supporting-data based on the predicting of the invasive mechanical ventilation weaning trajectory.S41. The method according to any of the preceding embodiments and with the featuresof embodiment S18, wherein the system is configured to output at least one decision-supporting-data based on the predicting of the invasive mechanical ventilation necessity trajectory.S42. The system according to any of the preceding embodiments, wherein the systemis configured to categorize at least one preprocessed data in at least one zonewherein further the at least one zone is delimited according to at least one range of values, wherein the at least one range of values is defined, with respect to a standardized database, to assess the necessity for invasive mechanical ventilation.S43. The system according to any of the preceding embodiments, wherein the systemis configured to categorize the at least one preprocessed data in at least one zonewherein further the at least one zone is delimited according to at least one range of values, wherein the at least one range of values is defined, with respect to a standardized database, to assess readiness for a Spontaneous Breathing Trial.S44. The system according to any of the preceding embodiments, wherein the systemis configured to categorize at least one preprocessing data in at least one zone wherein further the at least one zone is delimited according to at least one range of values, wherein the at least one range of values is defined, with respect to a standardized database, to assess if a Spontaneous Breathing Trial is successful.S45. The system according to any of the preceding embodiments, wherein the systemis configured to categorize at least one preprocessing data in at least one zonewherein further the at least one zone is delimited according to at least one range of values, wherein the at least one range of values is defined, with respect to a standardized database, to assess readiness for extubation.S46. The system according to any of the preceding embodiments, wherein the systemis configured to categorize at least one preprocessing data in at least one zonewherein further the at least one zone is delimited according to at least one range of values, wherein the at least one range of values, wherein the at least one range of values is defined, with respect to a standardized database, to assess readiness for weaning from mechanical ventilation.S47. The system according to any of the two preceding embodiments, wherein thesystem is configured to display the at least one preprocessed data with respect to the at least one zone it is classified in.S48. The system according to any of the preceding embodiments, wherein the systemis configured to output at least one signal related to the at least one preprocesseddata with respect to the at least one zone it is classified in.S49. The system according to any of the preceding embodiments with the featuresaccording to embodiment S36 wherein the system is configured to display at leastone successful weaning probability level in function of time.S50. The system according to any of the preceding embodiments with the featuresaccording to embodiment M38 wherein the system is configured to display at leastone risk level in function of time.S51. The system according to any of the preceding embodiments with the featuresaccording to any of embodiments S36 and M38 wherein the system is configuredto store at least one input submitted by an authorized user corresponding to the at least one successful weaning probability level and / or the at least one risk level at at least one time.S52. The system according to any of the preceding embodiments with the featuresaccording to any of embodiments S36 and M38 wherein the system is configuredto display at least one input submitted by an authorized user corresponding the at least one successful weaning probability level and / or the at least one risk level at at least one time.S53. The system according to any of the preceding embodiments, wherein the systemis configured to prompt at least one authorized user to input at least one data point.S54. The system according to the preceding embodiment, wherein the at least one datapoint comprises computer-readable input data points.S55. The system according to any of the two preceding embodiments, wherein the atleast one data point comprises at least one selectable data point comprising at least one predetermined select data point, wherein the method comprises prompting at least one authorized user to select at least one option.S56. The system according to any of the two preceding embodiments, wherein thesystem is configured to bidirectionally communicate with at least one server.S57. The system according to any of the preceding embodiments, wherein the systemis configured to utilize any data of any of the preceding embodiments to train at least one algorithm.S58. The system according to the two preceding embodiments, wherein the trainingstep is performed on the at least one server.S59. The system according to any of the preceding embodiment, wherein the system isconfigured to perform any of the steps of the method according to any of the preceding method embodiments in an integrated intensive care unit system.S60. The system according to any of the preceding embodiments and with the featuresof embodiment S56, wherein at least one of the at least one server comprises alocal server.S61. The system according to any of the preceding embodiments and with the featuresof embodiment S56, wherein at least one of the at least one server comprises a cloud server.S62. The system according to any of the preceding embodiments and with the featuresof embodiments S36, wherein the at least one successful weaning probability level comprises at least one successful weaning probability level threshold comprising at least one successful weaning probability score defining the at least one successful weaning probability level as Score Successful Weaning Probability LevelN.A. N.A. (-1)Score<Threshold / 2 Low (0)Score >= Threshold / 2 and Score<Threshold Medium (1)Score >= Threshold High (2)S63. The system according to any of the preceding embodiments and with the featuresof embodiments M38, wherein the at least one necessity probability levelcomprises at least one necessity probability level threshold comprising at least one necessity probability score defining the at least one necessity probability level as Score Necessity Probability LevelN.A. N.A. (-1)Score<Threshold / 2 Low (0)Score >= Threshold / 2 and Score<Threshold Medium (1)Score >= Threshold High (2)S64. The system according to any of the preceding system embodiments and with thefeatures of embodiment S7, wherein the system is configured to perform any of the steps of the method according to any of the preceding method embodiments by using at least one of the: first module, second module, third module, fourth module, fifth module, sixth module, seventh module and eighth module.S65. The system according to any of the previous system embodiments with thefeatures of embodiments S19, wherein the system is configured to diagnose a probability of successful weaning from invasive mechanical ventilation.S66. The system according to any of the previous system embodiments with thefeatures of embodiments S20, wherein the system is configured to diagnose a necessity for initiating invasive mechanical ventilation.S67. The system according to any of the preceding system embodiments wherein theretrieving component is configured to retrieve at least one data of the training ofthe at least one artificial-intelligence-assisted module.S68. The system according to any of the preceding system embodiments wherein theat least one artificial-intelligence-assisted module is configured to be trained usingthe at least one preprocessed data.S69. The system according to any of the preceding embodiments, wherein theretrieving component is configured to retrieve at least one ventilator-measuredinput parameter.S70. The system according to any of the preceding embodiments with the features ofembodiment M69, wherein the at least one ventilator-measured input comprises at least one of, tidal volume, positive end-expiratory pressure (PEEP), peak inspiratory pressure, respiratory rate, inspiratory flow and / or waveform, and / or plateau pressure.S71. The system according to any of the preceding system embodiments, wherein thecategorizing component is configured to generate at least one respiratorymechanics metric, comprised in the at least one invasive mechanical status data.S72. The system according to any of the preceding embodiments with the features ofembodiment S70, wherein the categorizing component is configured to generateat least one respiratory mechanics metrics according to at least one of the at least one ventilator-measured input.S73. The system according to any of the preceding embodiments with the features ofembodiment S71, wherein the at least one ventilator-measured input comprises at least one of, driving pressure, airway resistance and / or mechanical power delivered to a respiratory system.S74. The system according to any of the preceding system embodiments, with thefeatures of S71, wherein the system is configured to activate at least one operatingmode, wherein the at least one operating mode is configured to generate at leastone ventilation metric according to at least one respiratory mechanics metric.S75. The system according to any of the preceding system embodiments, with thefeatures of S74, wherein the system is configured to restrict the retrievingcomponent and / or categorizing component to be configured to retrieve therequired at least one respiratory mechanics metric and / or the at least one data used to generate the at least one respiratory mechanics metric, for the at least one activated operating mode to be working properly.S76. The system according to any of the preceding system embodiments, with thefeatures of S74, wherein the system is configured to activate a volume-controlledventilation operating mode, wherein the volume-controlled ventilation operating mode is configured to generate at least one mechanical power metric, wherein theat least one mechanical power metric comprises a metric of energy per unit time delivered by a ventilator.S77. The system according to any of the preceding system embodiments, with thefeatures of S70 and S76, wherein the volume-controlled ventilation operatingmode is configured to generate at least one mechanical power metric via at least one of, tidal volume, positive end-expiratory pressure (PEEP) and / or plateau pressure, respiratory rate, and / or inspiratory flow and / or waveform.S78. The system according to any of the preceding system embodiments, with thefeatures of M74, wherein the system is configured to activate a pressure-supportventilation operating mode, wherein the pressure-support ventilation operating mode is configured to generate at least one predicted muscular pressure (Pmus) metric and / or transpulmonary pressure metric.S79. The system according to any of the preceding system embodiments, with thefeatures of M4 and / or M6, and M78, wherein the pressure-support ventilationoperating mode is configured to generate at least one predicted muscular pressure (Pmus) metric and / or transpulmonary pressure metric via at least one of ventilator parameter data and / or at least one physiological data.S80. The system according to any of the preceding system embodiments, with thefeatures of M74, wherein the system is configured to activate a pressure-controlventilation operating mode, wherein the pressure-control ventilation operating mode is configured to generate at least one quantitative metric of respiratory mechanics and stress.S81. The system according to any of the preceding system embodiments, with thefeatures of M80, wherein at least one quantitative metric of respiratory mechanics and stress comprises at least one of dynamic lung compliance metric, driving pressure metric, derived indices of mechanical power, and / or derived indices of transpulmonary pressure.S82. The system according to any of the preceding system embodiments, with thefeatures of M70 and M80, wherein the pressure-control ventilation operating modeis configured to generate at least one quantitative metric of respiratory mechanics and stress via at least one delivered tidal volume, positive end-expiratory pressure(PEEP), and / or respiratory rate.S83. The system according to any of the preceding embodiments, with the features ofembodiments M73 and / or M76, wherein the system is configured to display atleast one component of any of the mechanical power delivered to a respiratory system and / or the at least one mechanical power metric.S84. The system according to any of the preceding embodiments, with the features ofembodiments M73 and / or M76, wherein the system is configured to display atleast one component of any of the mechanical power delivered to a respiratory system and / or the at least one mechanical power metric, as a percentage contribution to total power.S85. The system according to any of the preceding system embodiments, with thefeatures of embodiment M74, wherein the system is configured to classify the atleast one respiratory mechanics metric and / or the at least one ventilation metric in at least one zone wherein further the at least one zone is delimited according to at least one range of values, wherein the at least one range of values, wherein the at least one range of values is defined, with respect to a standardized database, to assess safety and / or risk levels.S86. The system according to any of the preceding system embodiments, with thefeatures of embodiment M74 wherein the system is configured to display the atleast one respiratory mechanics metric and / or the at least one ventilation metric.S87. The system according to any of the preceding system embodiments, with thefeatures of embodiment M85 wherein the system is configured to display the atleast one respiratory mechanics metric and / or the at least one ventilation metric with respect to the at least one zone it is classified in.S88. The system according to any of the preceding system embodiments, wherein thesystem is configured to output at least one signal related to the at least onerespiratory mechanics metric and / or the at least one ventilation metric with respect to the at least one zone it is classified in.S89. The system according to any of the preceding system embodiments, wherein thesystem is configured to simulate at least one ventilator simulation according to atleast one ventilator parameter data.S90. The system according to any of the preceding embodiments, with the features ofembodiment M89, wherein the system is configured to simulate the at least oneventilator when at least one ventilator parameter data is altered.S91. The system according to any of the preceding embodiments, with the features ofembodiment M74, wherein the system is configured to store the at least onerespiratory mechanics metric and / or the at least one ventilation metric.S92. The system according to any of the preceding embodiments, with the features ofembodiment M74, wherein the system is configured to export the at least onerespiratory mechanics metric and / or the at least one ventilation metric.S93. The system according to any of the preceding embodiments, with the features ofembodiment M74, wherein the system is configured to integrate the at least onerespiratory mechanics metric and / or the at least one ventilation metric into electronic health records.S94. A system for conducting a spontaneous breathing test, the system comprisinga retrieving component configured to retrieve at least one data from at least one data source, a processing component configured to preprocess the at least one data to automatically generate at least one preprocessed data,a comparing component configured to compare the at least one preprocessed datawith a standardized database.S95. The system according to the preceding embodiment, wherein the processingcomponent is configured to perform at least one measurement unit conversion.S96. The system according to any of preceding system embodiments S94-S95, whereinthe processing component is configured to discard at least one out-of-range value.S97. The system according to any of preceding system embodiments S94-S96, whereinthe processing component is configured to process at least one ventilator parameter data.S98. The system according to any of preceding system embodiments S94-S97, wherein the processing component is configured to process at least one blood chemistry data.S99. The system according to any of preceding system embodiments S94-S98, whereinthe processing component is configured to process at least one physiological data.S100. The system according to any of preceding system embodiments S94-S99, whereinthe system is configured to categorize the at least one preprocessed data in atleast one zone wherein further the at least one zone is delimited according to at least one range of values, wherein the at least one range of values is defined, with respect to a standardized database, to assess readiness for a Spontaneous Breathing Trial.S101. The system according to any of the preceding system embodiments S94-S100,wherein the system is configured to categorize the at least one preprocessed data in at least one zone wherein further the at least one zone is delimited according to at least one range of values, wherein the at least one range of values is defined, with respect to a standardized database, to assess if the Spontaneous Breathing Trial is successful.S102. The system according to any of preceding system embodiments S94-S100,wherein the system comprises a display component.S103. The system according to the preceding system embodiment, wherein the displaycomponent is configured to display the result of the comparing component.S104. The system according to any of preceding system embodiments S94-S103,wherein the retrieving component is configured to retrieve at least one respiratory rate data.S105. The system according to any of preceding system embodiments S94-S104,wherein the retrieving component is configured to retrieve at least one oxygen saturation (SpO2) data.S106. The system according to any of preceding system embodiments S94-S105,wherein the retrieving component is configured to retrieve at least one partialpressure of oxygen (PaO2) data.S107. The system according to any of preceding system embodiments S94-S106,wherein the retrieving component is configured to retrieve at least one heart rate data.S108. The system according to any of preceding system embodiments S94-S107,wherein the retrieving component is configured to retrieve at least one arrhythmia symptom status data.S109. The system according to any of preceding system embodiments S94-S108,wherein the retrieving component is configured to retrieve at least one myocardial symptom status data.S110. The system according to any of preceding system embodiments S94-S109,wherein the retrieving component is configured to retrieve at least one hypertensive urgency status data.S111. The system according to any of preceding system embodiments S94-S110,wherein the retrieving component is configured to retrieve at least one clinical symptom of respiratory distress status data.S112. The system according to any of preceding system embodiments S94-S111, withthe features of system embodiments S104, S105 or S106, S107, S108 or S109,S110, and S111 wherein the comparing component is configured to output acomparison result wherein the comparison result comprises a spontaneous breathing trial output.S113. A system, wherein the system comprises any of any combination of,the system according to any preceding embodiments S1-S93, andthe system according to any preceding embodiments S94-S112.M94. The method according to any of the preceding method embodiments, wherein themethod comprises utilizing the system according to any of the preceding system embodiments to carry out the method according to any of the preceding method embodiments.M95. The method according to any of the preceding method embodiments, wherein themethod comprises utilizing components of the system according to any of thepreceding system embodiments to carry out given steps of the method according to any of the preceding method embodiments.A19. The assessment method according to any of the preceding assessment methodembodiments, wherein the assessment method comprises utilizing the system according to any of the preceding system embodiments to carry out the assessment method according to any of the preceding assessment method embodiments.A20. The assessment method according to any of the preceding assessment methodembodiments, wherein the assessment method comprises utilizing components of the system according to any of the preceding system embodiments to carry outgiven steps of the assessment method according to any of the preceding assessment method embodiments.B2. The compound method according to any of the preceding compound methodembodiments, wherein the compound method comprises utilizing the system according to any of the preceding system embodiments to carry out the compound method according to any of the preceding compound method embodiments.B3. The compound method according to any of the preceding compound methodembodiments, wherein the compound method comprises utilizing components of the system according to any of the preceding system embodiments to carry outgiven steps of the compound method according to any of the preceding compound method embodiments. Below is a list of computer program embodiments. Those will be indicated with a letter “C”. Whenever such embodiments are referred to, this will be done by referring to “C” embodiments.C1. A computer program comprising instructions which, when the program is executedby a computer, cause the computer to carry out the method according to any of the preceding method embodiments.C2. A computer program comprising instructions which, when the program is executedby a computer, cause the computer to carry out the method according to any of the preceding assessment method embodiments.C3. A computer program comprising instructions which, when the program is executedby a computer, cause the computer to carry out the method according to any of the preceding compound method embodiments. Below is a list of computer storage embodiments. Those will be indicated with a letter “T”. Whenever such embodiments are referred to, this will be done by referring to “T” embodiments.T1. A non-transient computer-readable medium comprising instructions which, whenexecuted by a computer, cause the computer to carry out the method according to any of the preceding method embodiments.T2. A non-transient computer-readable medium comprising instructions which, whenexecuted by a computer, cause the computer to carry out the method according to any of the preceding automation method embodiments.T3. A non-transient computer-readable medium comprising instructions which, whenexecuted by a computer, cause the computer to carry out the method according to any of the preceding compound method embodiments. Below is a list of use embodiments. Those will be indicated with a letter “U”. Whenever such embodiments are referred to, this will be done by referring to “U” embodiments.U1. Use of the system according to any of the preceding system embodiments.U2. Use according to the preceding embodiment for carrying out the method accordingto any of the preceding method embodiments.U3. Use according to the preceding embodiment for carrying out the method accordingto any of the preceding automation method embodiments.U4. Use according to the preceding embodiment for carrying out the method accordingto any of the preceding compound method embodiments. Brief Figure description The present invention will now be described with reference to the accompanying drawings which illustrate embodiments of the invention. These embodiments should only exemplify, but not limit, the present invention.Fig. 1 schematically depicts a system for monitoring invasive mechanical ventilationfunction according to embodiments of the present invention;Fig. 2 schematically depicts an example of the system according to embodiment ofthe present invention;Fig. 3 schematically depicts an example of the system according to embodiments ofthe present invention implementing steps of the method according to embodiments of the present invention;Fig. 4 schematically depicts a computing device;Fig. 5 depicts a chart outputted by the present invention, representing the retrievedmeasurements of clinical parameters with respect to their corresponding urgency level zones;Fig. 6 depicts chart outputted by the present invention, representing the scorescalculated by the system as well as a note inputted into the system by an authorized user, with respect to time;Fig. 7 depicts a chart outputted by the present invention, representing the retrievedmeasurements of clinical parameters with respect to their corresponding SBT success level zones. It is noted that not all the drawings carry all the reference signs. Instead, in some of the drawings, some of the reference signs have been omitted for sake of brevity and simplicity of illustration. Embodiments of the present invention will now be described with reference to the accompanying drawings. Detailed description of drawings Fig. 1 schematically depicts a system 100 for monitoring invasive mechanical ventilation according to embodiments of the present invention. In simple terms, the system 100comprises an retrieving component 150, a processing component 190, and a activatingcomponent 180. Moreover, the system 100 comprises at least one artificial-intelligence-assisted module 200, such as at least one of or a combination of a first module 210, asecond module 220, a third module 230, a fourth module 240, a fifth module 250, a sixth module 260, a seventh module 270, and an eighth module 280. Fig. 1 also depicts a “Spontaneous Breathing Trial” system 300 for monitoring invasive mechanical ventilation,comprising an retrieving component 150, a processing component 190, a comparingcomponent 360, and an outputting component (not shown in this figure).The retrieving component 150 may be configured to retrieve at least one data from at leastone data source (not shown). Moreover, the processing component 190 may be configured to at least preprocess the at least one data to automatically generate at least onepreprocessed data.The activating component 180 may also be configured to activate at least one artificial-intelligence-assisted module 200 such as at least one of the first module 210, the secondmodule 220, the third module 230, the fourth module 240, the fifth module 250, the sixth module 260, the seventh module 270, and the eighth module 280. Additionally oralternatively, the activating component 180 may be configured to activate the at least oneartificial-intelligence-assisted module 200 according to the at least one invasive mechanicalventilation status data. Furthermore, it should be understood that components of the system 100 may be in bidirectional communication (not shown).The comparing component 360 may be configured to compare the information containedin the at least one preprocessed data with a standardized database. The preprocessing component may be shared between any combination of systems presented in the invention, for example, the preprocessing component 190 may be shared between system 100 and system 300. A preprocessing component may also be independent from other systems. Moreover, the system 100 is configured to perform the steps of the method as recited herein. The system 300 is configured to perform the steps of the assessment method as recited herein. The systems 100 and 300 are configured to perform the compound method as recited herein. It should be understood that in some embodiment, any of the component 150, 190, and 180 may be at least be partially integrated in a single component. For instance, the retrieving component 150 and the processing component 190 may be integrated into a single component. It should also be understood that in some embodiment, any of the component 150, 190, and 360 may be at least be partially integrated in a single component. Moreover, it should be understood that in some embodiment, any of the any components of any of the systems cited herein may be at least be partially integrated in a single component. The system 100 may perform steps of the method as exemplified in Fig. 2.Fig. 2 schematically depicts an example of the system 100 according to embodiments ofthe present invention. In Fig. 2, the example is depicted in relation to one artificial-intelligence-assisted module 200, in particular to the first module 210. However, it shouldbe understood that the system 100 according to the present invention is also configuredto operate with any or any combination of the at least one artificial-intelligence-assistedmodules 200 according to embodiments of the present invention.The system 100 may also be configured as a medical software system 100. The medicalsoftware system 100 may comprise a plurality of components, wherein the system 100may be configured to be deployed both on-premises in hospital servers or in the cloud for instance via a commercial cloud provider.Furthermore, the system 100 may be configured to deploy at least one model. Thedeployment view may focus on aspects of the system that are important for the system togo into live operation and defines the physical environment in which the system 100 isintended to run. For instance, Fig. 2 depicts the system 100 configured as an invasivemechanical ventilation platform system 100. Internal components of the system 100 arerepresented by the dashed linesand the communication direction between modules isrepresented by the arrows. It should be understood that the communication direction asdepicted in Fig. 2 is only exemplary, and that in other embodiments the communication direction may be the opposite as depicted in Fig.2 and / or it may be bidirectional. Moreover, the system may also be deployed in two different modalities, for example, on-premise such as in locally in hospital; and on-cloud such as a cloud application running in a commercially available environment, e.g., running in Microsoft Azure environment. In one embodiment,the system may be encapsulated in a virtual machine, e.g., in a single virtual machine. Inthe on-premise deployment, the system may be deployed as docker container.The system may implement the steps of the method according to embodiments of thepresent invention and may also prompt the modules as defined herein.Fig. 3 schematically depicts an example of the system implementing steps of method for monitoring invasive mechanical ventilation, according to embodiments of the present invention. In simple terms, the method for monitoring invasive mechanical ventilationcomprises three main steps, which may also be referred to as main sections, comprising:data acquisition S150, data preprocessing S190, and invocation of artificial-intelligence-assisted modules as needed S180. It should be understood that the method for monitoring invasive mechanical ventilation according to embodiments of the present invention is a computer-implemented method, which may further be executed in the medical software system.It should also be understood that the invocation S180 of the artificial-intelligence-assistedmodules comprises activating of at least one artificial-intelligence-assisted module 200,which may also be referred to as invoking of the at least one artificial-intelligence-assistedmodule, that is, invoking the needed artificial-intelligence pending on a necessity forinvasive mechanical ventilation output.The data acquisition S150 comprises the process in which the medical software system receives data from a plurality of sources, i.e., it is the step of the method for monitoringinvasive mechanical ventilation that prompts the medical software system to retrieve atleast one data from at least one source, wherein the at least one data comprises data of auser and related to invasive mechanical ventilation treatment. For instance, the methodmay prompt the medical software system to receive clinical data related to a patient admitted in an intensive care unit (ICU), wherein the clinical data may be associated with time and day of measurement and / or unit of measurement. Moreover, the clinical data may be received, for example. From a hospital electronic health record system such as from medical devices, laboratory information system, data manually input by medical care personal such as nurses and / or physicians. Clinical data may be received from the Hospital Electronic Health Record system, from medical devices, from laboratory informationsystem, manually inserted by the nurses, clinicians and / or physicians. The clinical datamay comprise, inter alia, but not limited to: blood chemistry measurements such as albumin, blood urea nitrogen (BUN), platelets’ count, creatinine, white blood cells count;physiological data such as diastolic pressure, systolic pressure, plateau pressure, heartrate, respiratory rate, urine output; patient information such as patient ID, height, weight, age, gender ethnicity, comorbidities, reason of admission to ICU; therapy data such as initial and final information of dialysis treatments.The preprocessing S190 comprises transforming the at least one data retrieved from theat least one data source into well-defined hourly-time-series data suitable to be used in subsequent steps of the method. Moreover, the processing comprises generating at leastone preprocessed data. For transforming the at least one data the preprocessing step maycomprise applying four distinct sequential phases. The preprocessing may be executed every hour taking as input all the measurements collected until a present hour. It should be understood that this step is stateless, which means that it processes every time all the data retrieved by the medical software system since a patient’s admission in ICU. In one embodiment, the preprocessing may comprise tabulating input for each parameter collected, from all a plurality of preprocessing phases such as blood chemistry, and physiologicaloutput. As an example, the following table is given, wherein the first timestamp corresponds to the admission hour of the patient, while the last timestamp is the current hour when the computation started. S0 TABLE: FINAL PROCESSING RESULTS Parameter Value Timestamp YYYY-MM-DD HH:00:00 YYYY-MM-DD (HH+1):00:00 …. YYYY-MM-DD (HH+n):00:00 Each phase of the preprocessing can handle missing values by carrying forward the value assigned to the preceding hour. This process runs at most max_gap times in a row, and this variable may assume different values depending on the type of treated parameter,e.g., blood chemistry, physiological output from 1 up to 96 hours. Put differently, a valueparameter may be carried out for a specific number of hours, depending on a given parameter. For instance, for urine output values the process may be carried forward for about 12, on the other hand, for creatine value this may be performed for a longer period such as for about 96 hours. The method may further be concerned with a plurality of other factors. For instance, given input data may potentially be subject to various problems such as insertion errors or different units of measurement. Hence, the method may also be concerned with data quality analysis which may comprise analyzing input data and performing, for instance, the following steps: A first step comprising unifying the unit of measurements S192 received to the ones usedby the system by performing measurement unit conversion where needed. E.g.:transforming volumetric units such liter into milliliter.A second step comprising discarding out-of-range values S194 according to a pre-definedlist compiled by the system yielding an output comprising cleaned data which may be used in subsequent steps of the preprocessing. In one embodiment, the preprocessing step may comprise urine output data preprocessing, comprising urine output data received by the medical software system which is assigned to a specific timestamp and within a specific hour and which may comprise different values. The preprocessing step yields a transformed value, for instance, millilitres values of urine transformed into milliliters / hour / kilograms values assigned to a specific hour. For this purpose, diuresis values are first converted to ml / h and then normalized by the adjustedideal body weight (AIBW) of the patient obtained with the following formulas:^^^^^ ^^^^ ^^^^ℎ^ (^^^)(^) =   50  +  (0.91  ×  [ℎ  −  152.4])^^^^^ ^^^^ ^^^^ℎ^ (^^^)(^) =   45.5  +  (0.91  ×  [ℎ  −  152.4])

[0018] (m = male, f = female, h = height in centimeters) ^^^^^^^^ ^^^^^ ^^^^ ^^^^ℎ^ (^^^^) = ^^^+ 0.4 ∗ (^^^− ^^^)

[0018] (ABW = actual body weight at the ICU admission) Such preprocessing of urine output data may be exemplified as follows: S1-1 TABLE: INPUT PARAMETERS EXAMPLE Parameter Value (ml) TimestampDiuresis 100 YYYY-MM-DD 11:12:04 Diuresis 300 YYYY-MM-DD 11:32:04 Diuresis 0 YYYY-MM-DD 11:52:04 Diuresis 200 YYYY-MM-DD 12:12:04 S1-2 TABLE: OUTPUT PARAMETERS EXAMPLE Parameter Value (ml / hr / kg) TimestampDiuresis XX YYYY-MM-DD 11:00:00 Diuresis XX YYYY-MM-DD 12:00:00 S1 ALGORITHM: URINE OUTPUT MAIN ROUTINE Input: List of diuresis measurements as S1-1 Table ordered by Timestamp keyOutput: Uniform time series of diuresis (ml / hr / kg) as S1-2 TableStep 1: For each timestamp T as current_hour_X in S1-2 Table: Step 2: Select all values between current_hour_X and current_hour_X-1 from S1-1 TableStep 3: If the result of Step 2 is empty, then proceed with S1-1 Algorithm. Otherwise go for S1-2 Algorithm Step 4: Repeat Step 1 S1-1 ALGORITHM: URINE OUTPUT MISSING VALUES Input: S1-Algorithm input and Step 2 of S1-AlgorithmOutput: Assign to the row of S1-2 Table where Timestamp is equal to current_hour_X avalue Step 1: Take the previous value assigned to the current_hour_X-1 in S1-2 TableStep 2: If counter is less than max_gap then assigned the value selected in Step 1 to the current_hour_X in S1-2 Table. Increment counter;Otherwise, assign Not Available to the current_hour_X in S1-2 Table.Step 3: Go to Step 1 of S1 AlgorithmS1-2 ALGORITHM: URINE OUTPUT CALCULATE HOURLY VALUE Input: S1-Algorithm input and Step 2 of S1-AlgorithmOutput: Assign to the row of S1-2 Table where Timestamp is equal to current_hour_X avalue Step 1: Insert in the result of Step 2 of S1-Algorithm the first measure preceding the(current_hour_X-1):00:00 from the S1-1 Table.Step 2: Calculates the time-gap between the values selected in Step 1. Step 3: For each gap obtained in Step 2 multiply the value of ml to the gap expressed in minutes and sum all these values. The result is the urine output expressed in ml / hr. Step 4: Assign to the current_hour_X in S1-2 Table the result obtained in Step 3 divided by the AIBW obtained with the formula. The result will be expressed as ml / hr / kg. Reset counter variable. Step 5: Go to Step 1 of S1 AlgorithmIn a further embodiment, the preprocessing S190 may also comprise preprocessing the atleast one data comprising blood chemistry data which are assigned to a specific timestamp and within a specific hour, which may comprise several different values. The preprocessingstep yields a selected singular value for each blood chemistry parameter collected, to awell-defined hour. S2-1 TABLE: INPUT PARAMETERS EXAMPLE Parameter Value Timestamp Creatinine 0.3 YYYY-MM-DD 11:12:04 Creatinine 0.5 YYYY-MM-DD 11:32:04 Creatinine 1.3 YYYY-MM-DD 11:52:04 Creatinine 1.1 YYYY-MM-DD 12:12:04The preprocessing of blood chemistry S196B data may be exemplified as follows:S2-2 TABLE: OUTPUT PARAMETERS EXAMPLE Parameter Value Timestamp Creatinine XX YYYY-MM-DD 11:00:00 Creatinine XX YYYY-MM-DD 12:00:00 S2 ALGORITHM: BLOOD CHEMISTRY MAIN ROUTINE Input: List of raw blood chemistry measurements as S2-1 Table ordered by Timestamp keyOutput: Uniform time series of the blood chemistry parameters as S2-2 TableStep 1: Group the S2-1 Table using Parameter column as key Step 2: For each timestamp T as current_hour_X in S2-2 Table, select all values between current_hour_X-1 and current_hour_X. Step 3: If the result of Step 2 is empty go for S2-1 Algorithm. Otherwise go for S2-2 Algorithm Step 4: Repeat Step 2 for each parameter grouped S2 –1 ALGORITHM: BLOOD CHEMISTRY MISSING VALUES Input: Input of Step 2 of the S2 Algorithm and current_hour_XOutput: Assign to the row of S2-2 Table where Timestamp is equal to current_hour_X a valueStep 1: Select the previous value of current_hour_X-1 from the S2-2 Table. Step 2: If counter is less than max_gap then assigned the value selected in Step 1 to the current_hour_X in S2-2 Table. Increment counter;Otherwise, assign Not Available to the current_hour_X in S2-2 Table. Step 3: Go to Step 2 of S2-Algorithm S2 –2 ALGORITHM: BLOOD CHEMISTRY HOURLY VALUE Input: Input of Step 2 of the S2 Algorithm and current_hour_XOutput: Assign to the row of S2-2 Table where Timestamp is equal to current_hour_X a valueStep 1: From the input of Step 2 of the S2 Algorihtm, the last value(meaning the one with maximum timestamp) will be selected and assigned tocurrent_hour_X hour.Reset counter variable. Step 2: Go to Step 2 of S2 AlgorithmIn another embodiment, the preprocessing S190 may also comprise preprocessing the atleast one data comprising physiological data from an Electronic Health Record system such as from a database of clinical data of a hospital, which are assigned a specific timestampand within a specific hour, which may comprise several different values. The preprocessingstep S190 yields a selected singular value for each physiological parameter collected to awell-defined hour. The preprocessing S190 of physiological data may be exemplified as follows: S3-1 TABLE: INPUT PARAMETERS EXAMPLE Parameter Value Timestamp Heart Rate 80 YYYY-MM-DD 11:12:04 Heart Rate 120 YYYY-MM-DD 11:32:04 Heart Rate 110 YYYY-MM-DD 11:52:04 Heart Rate 70 YYYY-MM-DD 12:12:04 S3-2 TABLE: OUTPUT PARAMETERS EXAMPLE Parameter Value Timestamp Heart Rate XX YYYY-MM-DD 11:00:00 Heart Rate XX YYYY-MM-DD 12:00:00

[0002] S3 ALGORITHM: PHYSIOLOGICAL PROCESSING MAIN ROUTINE Input: List of raw physiological measurements as S3-1 Table ordered by Timestamp keyOutput: Uniform time series of the physiological parameters as S3-2 TableStep 1: Group the S3-1 Table using Parameter column as key Step 2: For each timestamp T as current_hour_X in S3-1 Table, select all values betweencurrent_hour_X-1 and current_hour_X. Step 3: If the result of Step 2 is empty go for S3-1 Algorithm. Otherwise go for S3-2 Algorithm. Step 4: Repeat Step 2 for each parameter grouped S3 –1 ALGORITHM: PHYSIOLOGICAL MISSING VALUES Input: Input of Step 2 of the S3 Algorithm and current_hour_XOutput: Assign to the row of S3-2 Table where Timestamp is equal to current_hour_X a valueStep 1: Select the previous value of current_hour_X-1 from the S3-2 Table. Step 2: If counter is less than max_gap then assigned the value selected in Step 1 to the current_hour_X in S3-2 Table. Increment counter;Otherwise, assign Not Available to the current_hour_X in S3-2 Table. Step 3: Go to Step 2 of S3-AlgorithmS3 –2 ALGORITHM: PHYSIOLOGICAL HOURLY VALUE Input: Input of Step 2 of the S3 Algorithm and current_hour_XOutput: Assign to the row of S3-2 Table where Timestamp is equal to current_hour_X a valueStep 1: From the input of Step 2 of the S3 Algorihtm, calculate the average value. Theresult will be assigned to current_hour_X hour.Step 2:R Ge os te ot S co teu pnt 2er ofva Sr 3ia Ab ll ge o. rithmFig. 3 also depicts a future invasive mechanical ventilation weaning or necessity trajectoryperformed by the method for monitoring invasive mechanical ventilation according toembodiments of the present invention. For this the method comprises activating at leastone artificial-intelligence-assisted module which may comprises at least one of: Firstmodule, the second module, the third module, the fourth module, the fifth module, thesixth module, the seventh module, and the eighth module.Invasive Mechanical Ventilation AI-model activatedACTIVE First module, second module, third module,fourth moduleNOT ACTIVE Fifth module, sixth module, seventh module,eighth moduleThe first module may output a probability of the patient to be liberated from mechanicalventilation within the next 24 hours, at the current time. The first module may alsogenerate a risk level that indicates a severity of a patient’s status (that is, how bad the status of the patient is), and an explanation of the outcome. In one embodiment, such an explanation comprises a list of most features to the outcome, i.e., the features that contributed the most or that influenced the most the outcome. For this purpose, the firstmodule receives as input the at least one preprocessed data (comprising a format: hourlytime values) obtained from the preprocessing step S190. The first module may receivepreprocessed data comprising clinical parameters comprising at least one of: Creatinine,Urine output, Albumin, ALAT, Anion gap, Base excess, Bicarbonate, Bilirubin, Bun, Calcium ion, Cardiac output, Chloride, Dynamic compliance, CRP, CVP, DIASTOLIC P., Eosinophils, EtCO2, FiO2, Glucose, HCT, Heart rate, HGB, Intracranial pressure, Ventilatory ratio, Lactate, Magnesium, MV exp, Neutrophilis, PaO2, Pa / Fio invasive, Peak pressure, Plateau pressure, Platelets, Potassium, pH, pCO2, PT, PTT, PEEP, Pmean, Respiratory rate, Respiratory rate (set), RSBI, SaO2, Sodium, SYSTOLIC P., Temperature, Total protein, TV exp, TV (set), WBC, Sedation score. The first module may, for each clinical parameter,calculate at least one value of: average, maximum, minimum, standard deviation for atleast one time interval. The at least one calculation is based on, for each clinical parameter,at least one value of: average, maximum, minimum, standard deviation, difference,average of difference, maximum of difference, minimum of difference, and standarddeviation of difference wherein these may be calculated on time.In one embodiment, the first module may also comprise at least one artificial-intelligence-assisted module. Calculated features are sent in input to a machine-learning model trained to predict the risk output probability of the patient to be liberated from mechanical ventilation within the next 24 hours.The first AI module may be applied to the input feature, for example, only if at least oneof the previously cited clinical parameters is present. The first module may be based on aXGBoost algorithm which is based on a set of decision tree learning algorithms that uses a gradient boosting framework. The output of the model may be a number between 0 and 1, wherein said output may be multiplied by 100 to represent the probability of the patient to be liberated from mechanical ventilation within the next 24 hours. However, it should beunderstood that the first module may be based on a plurality of different algorithmarchitectures, such as: Ensemble Decision trees; Random Forest; Gradient boosting decision tree; XGboost; Neural Network; Recurrent Neural Network; Convolutional Neural Network.In a further embodiment, the first module may also generate a risk level. The first modulemay compare its output with a specified threshold, wherein the method may comprise assigning and defining a new risk level variable according to these rules: Score Risk levelN.A. N.A. (-1)Score<Threshold / 2 Low (0)Score >= Threshold / 2 and Score<Threshold Medium (1)Score >= Threshold High (2)Furthermore, the first module may also activate an initiation of a Spontaneous BreathingTrial (SBT) or other procedures according to the probability generated by the first module with respect to a certain threshold.Moreover, the first module may also automatically generate a list of features, variables,with their weights (expressed in terms of %), that most influenced the output of the firstmodule. For this purpose, the first module may execute at least one computer-implemented method comprising at least one of: SHapley Additive exPlanations; Impurity- based feature importance; Permutation Importance; Partial dependence plots; LocalInterpretable Model-Agnostic Explanations; Individual conditional explanation.The second module may automatically output a probability of the patient to be liberatedfrom mechanical ventilation within the next 48 hours, at the current time. Moreover, thesecond module may also generate a risk level that a severity of a patient’s status indicates(that is, how bad the status of the patient is) and an explanation of the outcome. In one embodiment, such an explanation comprises a list of most features to the outcome, i.e., the features that contributed the most or that influenced the most the outcome. For thispurpose, the second module receives as input the at least one preprocessed data(comprising a format: hourly time values) obtained from the preprocessing step S190calculated. The second module may receive preprocessed data comprising clinicalparameters comprising at least one of: Creatinine, Urine output, Albumin, ALAT, Anion gap, Base excess, Bicarbonate, Bilirubin, Bun, Calcium ion, Cardiac output, Chloride, Dynamic compliance, CRP, CVP, DIASTOLIC P., Eosinophils, EtCO2, FiO2, Glucose, HCT, Heart rate, HGB, Intracranial pressure, Ventilatory ratio, Lactate, Magnesium, MV exp, Neutrophilis, PaO2, Pa / Fio invasive, Peak pressure, Plateau pressure, Platelets, Potassium, pH, pCO2, PT, PTT, PEEP, Pmean, Respiratory rate, Respiratory rate (set), RSBI, SaO2, Sodium,SYSTOLIC P., Temperature, Total protein, TV exp, TV (set), WBC, Sedation score. Thesecond module may, for each clinical parameter, calculate at least one value of: average,maximum, minimum, standard deviation for at least one time interval. The at least onecalculation is based on, for each clinical parameter, at least one value of: average,maximum, minimum, standard deviation, difference, average of difference, maximum ofdifference, minimum of difference, and standard deviation of difference wherein these maybe calculated on time.In one embodiment, the second module may also comprise at least one artificial-intelligence-assisted module. Calculated features are sent in input to a machine-learning model trained to predict the probability of the patient to be liberated from mechanicalventilation within the next 48 hours. The second module may be applied to the inputfeature, for example, only if at least one of the previously cited clinical parameters ispresent. The second module may be based on a XGBoost algorithm that is based on a setof decision tree learning algorithms that uses a gradient boosting framework. The outputof the model is a number between 0 and 1, and it is multiplied by 100 to represent theprobability of the patient to be liberated from mechanical ventilation within the next 48hours. The second module may be based on different algorithm architectures, inter alia,but not limited to: Ensemble Decision trees; Random Forest; Gradient boosting decision tree; XGboost; Neural Network; Recurrent Neural Network; Convolutional Neural Network;In a further embodiment, the second module may also generate a risk level. The secondmodule may compare its output with a specified threshold, wherein the method maycomprise assigning and defining a new risk level variable according to these rules: Score Risk levelN.A. N.A. (-1)Score<Threshold / 2 Low (0)Score >= Threshold / 2 and Score<Threshold Medium (1)Score >= Threshold High (2)Furthermore, the second module may also activate an initiation of a Spontaneous BreathingTrial (SBT) or other procedures according to the probability generated by the second module with respect to a certain threshold.Moreover, the second module may also automatically generate a list of features, variables,with their weights (expressed in terms of %), that most influenced the output of the secondmodule. For this purpose, the second module may execute at least one computer-implemented method comprising at least one of: SHapley Additive exPlanations; Impurity- based feature importance; Permutation Importance; Partial dependence plots; Local Interpretable Model-Agnostic Explanations; Individual conditional explanation.The third module may automatically output a probability of the patient to be liberated frommechanical ventilation within the next 72 hours, at the current time. Moreover, the thirdmodule may also generate a risk level that a severity of a patient’s status indicates (thatis, how bad the status of the patient is) and an explanation of the outcome. In one embodiment, such an explanation comprises a list of most features to the outcome, i.e., the features that contributed the most or that influenced the most the outcome. For thispurpose, the third module receives as input the at least one preprocessed data (comprisinga format: hourly time values) obtained from the preprocessing step S190 calculated. Thethird module may receive preprocessed data comprising clinical parameters comprising atleast one of: Creatinine, Urine output, Albumin, ALAT, Anion gap, Base excess, Bicarbonate, Bilirubin, Bun, Calcium ion, Cardiac output, Chloride, Dynamic compliance, CRP, CVP, DIASTOLIC P., Eosinophils, EtCO2, FiO2, Glucose, HCT, Heart rate, HGB, Intracranial pressure, Ventilatory ratio, Lactate, Magnesium, MV exp, Neutrophilis, PaO2, Pa / Fio invasive, Peak pressure, Plateau pressure, Platelets, Potassium, pH, pCO2, PT, PTT, PEEP, Pmean, Respiratory rate, Respiratory rate (set), RSBI, SaO2, Sodium, SYSTOLIC P.,Temperature, Total protein, TV exp, TV (set), WBC, Sedation score. The third module may,for each clinical parameter, calculate at least one value of: average, maximum, minimum,standard deviation for at least one time interval. The at least one calculation is based on,for each clinical parameter, at least one value of: average, maximum, minimum, standarddeviation, difference, average of difference, maximum of difference, minimum ofdifference, and standard deviation of difference wherein these may be calculated on time.In one embodiment, the third module may also comprise at least one artificial-intelligence-assisted module. Calculated features are sent in input to a machine-learning model trained to predict the probability of the patient to be liberated from mechanical ventilation withinthe next 72 hours. The third module may be applied to the input feature, for example, onlyif at least one of the previously cited clinical parameters is present. The third module maybe based on a XGBoost algorithm that is based on a set of decision tree learning algorithms that uses a gradient boosting framework. The output of the model is a number between 0 and 1, and it is multiplied by 100 to represent the probability of the patient to be liberatedfrom mechanical ventilation within the next 72 hours. The third module may be based on different algorithm architectures, inter alia, but not limited to: Ensemble Decision trees; Random Forest; Gradient boosting decision tree; XGboost; Neural Network; Recurrent Neural Network; Convolutional Neural Network; In a further embodiment, the third modulemay also generate a risk level. The third module may compare its output with a specifiedthreshold, wherein the method may comprise assigning and defining a new risk level variable according to these rules: Score Risk levelN.A. N.A. (-1)Score<Threshold / 2 Low (0)Score >= Threshold / 2 and Score<Threshold Medium (1)Score >= Threshold High (2)Furthermore, the third module may also activate an initiation of a Spontaneous BreathingTrial (SBT) or other procedures according to the probability generated by the third module with respect to a certain threshold.Moreover, the third module may also automatically generate a list of features, variables,with their weights (expressed in terms of %), that most influenced the output of the thirdmodule. For this purpose, the third module may execute at least one computer-implemented method comprising at least one of: SHapley Additive exPlanations; Impurity- based feature importance; Permutation Importance; Partial dependence plots; Local Interpretable Model-Agnostic Explanations; Individual conditional explanation.The fourth module may automatically output the prediction of the number of hours beforethe patient can be successfully liberated from mechanical ventilation, at the current time.Moreover, the fourth module may also generate a risk level that a severity of a patient’sstatus indicates (that is, how bad the status of the patient is) and an explanation of theoutcome. In one embodiment, such an explanation comprises a list of most features to the outcome, i.e., the features that contributed the most or that influenced the most theoutcome. For this purpose, the fourth module receives as input the at least onepreprocessed data (comprising a format: hourly time values) obtained from thepreprocessing step S190 calculated. The fourth module may receive preprocessed datacomprising clinical parameters comprising at least one of: Creatinine, Urine output, Albumin, ALAT, Anion gap, Base excess, Bicarbonate, Bilirubin, Bun, Calcium ion, Cardiac output, Chloride, Dynamic compliance, CRP, CVP, DIASTOLIC P., Eosinophils, EtCO2, FiO2, Glucose, HCT, Heart rate, HGB, Intracranial pressure, Ventilatory ratio, Lactate, Magnesium, MV exp, Neutrophilis, PaO2, Pa / Fio invasive, Peak pressure, Plateau pressure, Platelets, Potassium, pH, pCO2, PT, PTT, PEEP, Pmean, Respiratory rate, Respiratory rate (set), RSBI, SaO2, Sodium, SYSTOLIC P., Temperature, Total protein, TV exp, TV (set),WBC, Sedation score. The fourth module may, for each clinical parameter, calculate at leastone value of: average, maximum, minimum, standard deviation for at least one timeinterval. The at least one calculation is based on, for each clinical parameter, at least onevalue of: average, maximum, minimum, standard deviation, difference, average ofdifference, maximum of difference, minimum of difference, and standard deviation ofdifference wherein these may be calculated on time.In one embodiment, the fourth module may also comprise at least one artificial-intelligence-assisted module. Calculated features are sent in input to a machine-learning model trained to predict the number of hours before the patient can be successfullyliberated from mechanical ventilation. The fourth module may be applied to the inputfeature, for example, only if at least one of the previously cited clinical parameters ispresent. The fourth module may be based on a XGBoost algorithm that is based on a setof decision tree learning algorithms that uses a gradient boosting framework. The outputof the model is a number between 0 and 1, and it is multiplied by 100 to represent the probability of the patient to be liberated from mechanical ventilation within the number ofhours determined by the fourth module. The fourth module may be based on differentalgorithm architectures, inter alia, but not limited to: Ensemble Decision trees; Random Forest; Gradient boosting decision tree; XGboost; Neural Network; Recurrent NeuralNetwork; Convolutional Neural Network; In a further embodiment, the fourth module mayalso generate a risk level. The fourth module may compare its output with a specifiedthreshold, wherein the method may comprise assigning and defining a new risk level variable according to these rules: Score Risk levelN.A. N.A. (-1)Score<Threshold / 2 Low (0)Score >= Threshold / 2 and Score<Threshold Medium (1)Score >= Threshold High (2)Furthermore, the fourth module may also activate an initiation of a Spontaneous BreathingTrial (SBT) or other procedures according to the probability generated by the fourth module with respect to a certain threshold.Moreover, the fourth module may also automatically generate a list of features, variables,with their weights (expressed in terms of %), that most influenced the output of the fourthmodule. For this purpose, the fourth module may execute at least one computer-implemented method comprising at least one of: SHapley Additive exPlanations; Impurity- based feature importance; Permutation Importance; Partial dependence plots; Local Interpretable Model-Agnostic Explanations; Individual conditional explanation.The fifth module may automatically output a probability of the patient to requiremechanical ventilation within the next 24 hours, at the current time. Moreover, the fifthmodule may also generate a risk level that a severity of a patient’s status indicates (thatis, how bad the status of the patient is) and an explanation of the outcome. In oneembodiment, such an explanation comprises a list of most features to the outcome, i.e.,the features that contributed the most or that influenced the most the outcome. For thispurpose, the fifth module receives as input the at least one preprocessed data (comprisinga format: hourly time values) obtained from the preprocessing step S190 calculated. Thefifth module may receive preprocessed data comprising clinical parameters comprising atleast one of: Creatinine, Urine output, Albumin, ALAT, Anion gap, Base excess, Bicarbonate, Bilirubin, Bun, Calcium ion, Cardiac output, Chloride, Dynamic compliance, CRP, CVP, DIASTOLIC P., Eosinophils, EtCO2, FiO2, Glucose, HCT, Heart rate, HGB, Intracranial pressure, Ventilatory ratio, Lactate, Magnesium, MV exp, Neutrophilis, PaO2, Pa / Fio invasive, Peak pressure, Plateau pressure, Platelets, Potassium, pH, pCO2, PT, PTT, PEEP, Pmean, Respiratory rate, Respiratory rate (set), RSBI, SaO2, Sodium, SYSTOLIC P.,Temperature, Total protein, TV exp, TV (set), WBC, Sedation score. The fifth module may,for each clinical parameter, calculate at least one value of: average, maximum, minimum,standard deviation for at least one time interval. The at least one calculation is based on,for each clinical parameter, at least one value of: average, maximum, minimum, standarddeviation, difference, average of difference, maximum of difference, minimum ofdifference, and standard deviation of difference wherein these may be calculated on time.In one embodiment, the fifth module may also comprise at least one artificial-intelligence-assisted module. Calculated features are sent in input to a machine-learning model trained to predict the probability of the patient to require mechanical ventilation within the next24 hours. The fifth module may be applied to the input feature, for example, only if at leastone of the previously cited clinical parameters is present. The fifth module may be basedon a XGBoost algorithm that is based on a set of decision tree learning algorithms that uses a gradient boosting framework. The output of the model is a number between 0 and 1, and it is multiplied by 100 to represent the probability of the patient to requiremechanical ventilation within the next 24 hours. The fifth module may be based on differentalgorithm architectures, inter alia, but not limited to: Ensemble Decision trees; Random Forest; Gradient boosting decision tree; XGboost; Neural Network; Recurrent NeuralNetwork; Convolutional Neural Network; In a further embodiment, the fifth module mayalso generate a risk level. The fifth module may compare its output with a specifiedthreshold, wherein the method may comprise assigning and defining a new risk level variable according to these rules: Score Risk levelN.A. N.A. (-1)Score<Threshold / 2 Low (0)Score >= Threshold / 2 and Score<Threshold Medium (1)Score >= Threshold High (2)Moreover, the fifth module may also automatically generate a list of features, variables,with their weights (expressed in terms of %), that most influenced the output of the fifthmodule. For this purpose, the fifth module may execute at least one computer-implemented method comprising at least one of: SHapley Additive exPlanations; Impurity- based feature importance; Permutation Importance; Partial dependence plots; Local Interpretable Model-Agnostic Explanations; Individual conditional explanation.The sixth module may automatically output a probability of the patient to requiremechanical ventilation within the next 48 hours, at the current time. Moreover, the sixthmodule may also generate a risk level that a severity of a patient’s status indicates (thatis, how bad the status of the patient is) and an explanation of the outcome. In one embodiment, such an explanation comprises a list of most features to the outcome, i.e., the features that contributed the most or that influenced the most the outcome. For thispurpose, the sixth module receives as input the at least one preprocessed data (comprisinga format: hourly time values) obtained from the preprocessing step S190 calculated. Thesixth module may receive preprocessed data comprising clinical parameters comprising atleast one of: Creatinine, Urine output, Albumin, ALAT, Anion gap, Base excess, Bicarbonate,Bilirubin, Bun, Calcium ion, Cardiac output, Chloride, Dynamic compliance, CRP, CVP,DIASTOLIC P., Eosinophils, EtCO2, FiO2, Glucose, HCT, Heart rate, HGB, Intracranial pressure, Ventilatory ratio, Lactate, Magnesium, MV exp, Neutrophilis, PaO2, Pa / Fio invasive, Peak pressure, Plateau pressure, Platelets, Potassium, pH, pCO2, PT, PTT, PEEP, Pmean, Respiratory rate, Respiratory rate (set), RSBI, SaO2, Sodium, SYSTOLIC P.,Temperature, Total protein, TV exp, TV (set), WBC, Sedation score. The sixth module may,for each clinical parameter, calculate at least one value of: average, maximum, minimum,standard deviation for at least one time interval. The at least one calculation is based on,for each clinical parameter, at least one value of: average, maximum, minimum, standarddeviation, difference, average of difference, maximum of difference, minimum ofdifference, and standard deviation of difference wherein these may be calculated on time.In one embodiment, the sixth module may also comprise at least one artificial-intelligence-assisted module. Calculated features are sent in input to a machine-learning model trained to predict the probability of the patient to require mechanical ventilation within the next48 hours. The sixth module may be applied to the input feature, for example, only if atleast one of the previously cited clinical parameters is present. The sixth module may bebased on a XGBoost algorithm that is based on a set of decision tree learning algorithms that uses a gradient boosting framework. The output of the model is a number between 0 and 1, and it is multiplied by 100 to represent the probability of the patient to requiremechanical ventilation within the next 48 hours. The sixth module may be based ondifferent algorithm architectures, inter alia, but not limited to: Ensemble Decision trees;Random Forest; Gradient boosting decision tree; XGboost; Neural Network; RecurrentNeural Network; Convolutional Neural Network; In a further embodiment, the sixth modulemay also generate a risk level. The sixth module may compare its output with a specifiedthreshold, wherein the method may comprise assigning and defining a new risk level variable according to these rules: Score Risk levelN.A. N.A. (-1)Score<Threshold / 2 Low (0)Score >= Threshold / 2 and Score<Threshold Medium (1)Score >= Threshold High (2)Moreover, the sixth module may also automatically generate a list of features, variables,with their weights (expressed in terms of %), that most influenced the output of the sixthmodule. For this purpose, the sixth module may execute at least one computer-implemented method comprising at least one of: SHapley Additive exPlanations; Impurity- based feature importance; Permutation Importance; Partial dependence plots; Local Interpretable Model-Agnostic Explanations; Individual conditional explanation.The seventh module may automatically output a probability of the patient to requiremechanical ventilation within the next 72 hours, at the current time. Moreover, the seventhmodule may also generate a risk level that a severity of a patient’s status indicates (thatis, how bad the status of the patient is) and an explanation of the outcome. In one embodiment, such an explanation comprises a list of most features to the outcome, i.e.,the features that contributed the most or that influenced the most the outcome. For thispurpose, the seventh module receives as input the at least one preprocessed data(comprising a format: hourly time values) obtained from the preprocessing step S190calculated. The seventh module may receive preprocessed data comprising clinicalparameters comprising at least one of: Creatinine, Urine output, Albumin, ALAT, Anion gap, Base excess, Bicarbonate, Bilirubin, Bun, Calcium ion, Cardiac output, Chloride, Dynamic compliance, CRP, CVP, DIASTOLIC P., Eosinophils, EtCO2, FiO2, Glucose, HCT, Heart rate, HGB, Intracranial pressure, Ventilatory ratio, Lactate, Magnesium, MV exp, Neutrophilis, PaO2, Pa / Fio invasive, Peak pressure, Plateau pressure, Platelets, Potassium, pH, pCO2, PT, PTT, PEEP, Pmean, Respiratory rate, Respiratory rate (set), RSBI, SaO2, Sodium,SYSTOLIC P., Temperature, Total protein, TV exp, TV (set), WBC, Sedation score. Theseventh module may, for each clinical parameter, calculate at least one value of: average,maximum, minimum, standard deviation for at least one time interval. The at least onecalculation is based on, for each clinical parameter, at least one value of: average,maximum, minimum, standard deviation, difference, average of difference, maximum ofdifference, minimum of difference, and standard deviation of difference wherein these maybe calculated on time.In one embodiment, the seventh module may also comprise at least one artificial-intelligence-assisted module. Calculated features are sent in input to a machine-learning model trained to predict the probability of the patient to require mechanical ventilationwithin the next 72 hours. The seventh module may be applied to the input feature, forexample, only if at least one of the previously cited clinical parameters is present. Theseventh module may be based on a XGBoost algorithm that is based on a set of decisiontree learning algorithms that uses a gradient boosting framework. The output of the model is a number between 0 and 1, and it is multiplied by 100 to represent the probability of the patient to require mechanical ventilation within the next 72 hours. The seventh module may be based on different algorithm architectures, inter alia, but not limited to: Ensemble Decision trees; Random Forest; Gradient boosting decision tree; XGboost; Neural Network; Recurrent Neural Network; Convolutional Neural Network; In a further embodiment, theseventh module may also generate a risk level. The seventh module may compare itsoutput with a specified threshold, wherein the method may comprise assigning and defininga new risk level variable according to these rules: Score Risk levelN.A. N.A. (-1)Score<Threshold / 2 Low (0)Score >= Threshold / 2 and Score<Threshold Medium (1)Score >= Threshold High (2)Moreover, the seventh module may also automatically generate a list of features, variables,with their weights (expressed in terms of %), that most influenced the output of theseventh module. For this purpose, the seventh module may execute at least one computer-implemented method comprising at least one of: SHapley Additive exPlanations; Impurity- based feature importance; Permutation Importance; Partial dependence plots; Local Interpretable Model-Agnostic Explanations; Individual conditional explanation.The eighth module may automatically output the prediction of the number of hours beforethe patient can require mechanical ventilation, at the current time. Moreover, the eighthmodule may also generate a risk level that a severity of a patient’s status indicates (thatis, how bad the status of the patient is) and an explanation of the outcome. In one embodiment, such an explanation comprises a list of most features to the outcome, i.e., the features that contributed the most or that influenced the most the outcome. For thispurpose, the eighth module receives as input the at least one preprocessed data(comprising a format: hourly time values) obtained from the preprocessing step S190calculated. The eighth module may receive preprocessed data comprising clinicalparameters comprising at least one of: Creatinine, Urine output, Albumin, ALAT, Anion gap, Base excess, Bicarbonate, Bilirubin, Bun, Calcium ion, Cardiac output, Chloride, Dynamic compliance, CRP, CVP, DIASTOLIC P., Eosinophils, EtCO2, FiO2, Glucose, HCT, Heart rate,HGB, Intracranial pressure, Ventilatory ratio, Lactate, Magnesium, MV exp, Neutrophilis,PaO2, Pa / Fio invasive, Peak pressure, Plateau pressure, Platelets, Potassium, pH, pCO2, PT, PTT, PEEP, Pmean, Respiratory rate, Respiratory rate (set), RSBI, SaO2, Sodium,SYSTOLIC P., Temperature, Total protein, TV exp, TV (set), WBC, Sedation score. The eighthmodule may, for each clinical parameter, calculate at least one value of: average,maximum, minimum, standard deviation for at least one time interval. The at least onecalculation is based on, for each clinical parameter, at least one value of: average,maximum, minimum, standard deviation, difference, average of difference, maximum ofdifference, minimum of difference, and standard deviation of difference wherein these maybe calculated on time.In one embodiment, the eighth module may also comprise at least one artificial-intelligence-assisted module. Calculated features are sent in input to a machine-learning model trained to predict the number of hours before the patient can require mechanicalventilation. The eighth module may be applied to the input feature, for example, only if atleast one of the previously cited clinical parameters is are present. The eighth module maybe based on a XGBoost algorithm that is based on a set of decision tree learning algorithms that uses a gradient boosting framework. The output of the model is a number between 0 and 1, and it is multiplied by 100 to represent the probability of the patient to require mechanical ventilation within the number of hours determined by the eighth module. Theeighth module may be based on different algorithm architectures, inter alia, but not limitedto: Ensemble Decision trees; Random Forest; Gradient boosting decision tree; XGboost; Neural Network; Recurrent Neural Network; Convolutional Neural Network; In a furtherembodiment, the eighth module may also generate a risk level. The eighth module maycompare its output with a specified threshold, wherein the method may comprise assigning and defining a new risk level variable according to these rules: Score Risk levelN.A. N.A. (-1)Score<Threshold / 2 Low (0)Score >= Threshold / 2 and Score<Threshold Medium (1)Score >= Threshold High (2)Moreover, the eighth module may also automatically generate a list of features, variables,with their weights (expressed in terms of %), that most influenced the output of the eighthmodule. For this purpose, the eighth module may execute at least one computer-implemented method comprising at least one of: SHapley Additive exPlanations; Impurity- based feature importance; Permutation Importance; Partial dependence plots; Local Interpretable Model-Agnostic Explanations; Individual conditional explanation.The at least one AI-assisted module may activate the spontaneous breathing trial module.A number of clinical parameters such as blood parameter data may also activate aninitiation of the spontaneous breathing trial module should these clinical parameters fall within a predetermined range according to a standardized database. The spontaneous breathing trial may be thus be configured to perform the method according to the assessment method embodiments.Fig. 4 depicts a schematic of a computing device 1000. The computing device 1000 maycomprise a computing unit 35, a first data storage unit 30A, a second data storage unit 30B and a third data storage unit 30C. The computing device 1000 can be a single computing device or an assembly of computing devices. The computing device 1000 can be locally arranged or remotely, such as a cloud solution. On the different data storage units 30 different data can be stored, such as the blood chemistry, physiological, and / or urine output related data on the first data storage 30A, the time stamped data and / or event code data and / or phenotypic data on the second data storage 30B and privacy sensitive data, such as the connection of the before-mentioned data to an individual, on the thirds data storage 30C. Additional data storage can be also provided and / or the ones mentioned before can be combined at least in part. Another data storage (not shown) can comprise data specifying for instance, clinical parameter data. This data can also be provided on one or more of the before-mentioned data storages.The computing unit 35 can access the first data storage unit 30A, the second data storageunit 30B and the third data storage unit 30C through the internal communication channel 160, which can comprise a bus connection 160. The computing unit 30 may be single processor or a plurality of processors, and may be, but not limited to, a CPU (central processing unit), GPU (graphical processing unit), DSP (digital signal processor), APU (accelerator processing unit), ASIC (application-specific integrated circuit), ASIP (application-specific instruction-set processor) or FPGA (fieldprogramable gate array). The first data storage unit 30A may be singular or plural, andmay be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). The second data storage unit 30B may be singular or plural, and may be, but not limitedto, a volatile or non-volatile memory, such as a random-access memory (RAM), DynamicRAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). The third data storage unit 30C may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). It should be understood that generally, the first data storage unit 30A (also referred to as encryption key storage unit 30A), the second data storage unit 30B (also referred to as data share storage unit 30B), and the third data storage unit 30C (also referred to as decryption key storage unit 30C) can also be part of the same memory. That is, only one general data storage unit 30 per device may be provided, which may be configured to store the respective encryption key (such that the section of the data storage unit 30 storing the encryption key may be the encryption key storage unit 30A), the respective data element share (such that the section of the data storage unit 30 storing the data element share may be the data share storage unit 30B), and the respective decryption key (such that the section of the data storage unit 30 storing the decryption key may be the decryption key storage unit 30A). In some embodiments, the third data storage unit 30C can be a secure memory device30C, such as, a self-encrypted memory, hardware-based full disk encryption memory andthe like which can automatically encrypt all of the stored data. The data can be decrypted from the memory component only upon successful authentication of the party requiring to access the third data storage unit 30C, wherein the party can be a user, computing device, processing unit and the like. In some embodiments, the third data storage unit 30C can only be connected to the computing unit 35 and the computing unit 35 can be configured to never output the data received from the third data storage unit 30C. This can ensure asecure storing and handling of the encryption key (i.e., private key) stored in the third datastorage unit 30C. In some embodiments, the second data storage unit 30B may not be provided but instead the computing device 1000 can be configured to receive a corresponding encrypted share from the database 60. In some embodiments, the computing device 1000 may comprise the second data storage unit 30B and can be configured to receive a corresponding encrypted share from the database 60. The computing device 1000 may comprise a further memory component 140 which may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory,such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous DynamicRAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). The memory component 140 may also be connected with the other components of the computing device 1000 (such as the computing component 35) through the internal communication channel 160. Further the computing device 1000 may comprise an external communication component 130. The external communication component 130 can be configured to facilitate sending and / or receiving data to / from an external device (e.g., backup device 10, recovery device 20, database 60). The external communication component 130 may comprise an antenna (e.g., WIFI antenna, NFC antenna, 2G / 3G / 4G / 5G antenna and the like), USB port / plug, LAN port / plug, contact pads offering electrical connectivity and the like. The external communication component 130 can send and / or receive data based on a communication protocol which can comprise instructions for sending and / or receiving data. Said instructions can be stored in the memory component 140 and can be executed by the computing unit 35 and / or external communication component 130. The external communication component 130 can be connected to the internal communication component 160. Thus, data received by the external communication component 130 can be provided to the memory component 140, computing unit 35, first data storage unit 30A and / or second data storage unit 30B and / or third data storage unit 30C. Similarly, data stored on the memory component 140, first data storage unit 30A and / or second data storage unit 30B and / or third data storage unit 30C and / or data generated by the commuting unit 35 can be provided to the external communication component 130 for being transmitted to an external device. In addition, the computing device 1000 may comprise an input user interface 110 which can allow the user of the computing device 1000 to provide at least one input (e.g.,instruction) to the computing device 100. For example, the input user interface 110 maycomprise a button, keyboard, trackpad, mouse, touchscreen, joystick and the like. Additionally, still, the computing device 1000 may comprise an output user interface 120 which can allow the computing device 1000 to provide indications to the user. For example, the output user interface 110 may be a LED, a display, a speaker and the like. The output and the input user interface 100 may also be connected through the internalcommunication component 160 with the internal component of the device 100.The processor may be singular or plural, and may be, but not limited to, a CPU, GPU, DSP,APU, or FPGA. The memory may be singular or plural, and may be, but not limited to,being volatile or non-volatile, such an SDRAM, DRAM, SRAM, Flash Memory, MRAM, F-RAM, or P-RAM. The data processing device can comprise means of data processing, such as, processor units, hardware accelerators and / or microcontrollers. The data processing device 20 can comprise memory components, such as, main memory (e.g., RAM), cache memory (e.g., SRAM) and / or secondary memory (e.g., HDD, SDD). The data processing device can comprise busses configured to facilitate data exchange between components of the data processing device, such as, the communication between the memory components and the processing components. The data processing device can comprise network interface cards that can be configured to connect the data processing device to a network, such as, to the Internet. The data processing device can comprise user interfaces, such as: -output user interface, such as:o screens or monitors configured to display visual data (e.g., displaying graphical userinterfaces of railway network status),Fig. 4 depicts a schematic of a computing device 1000. The computing device 1000 maycomprise a computing unit 35, a first data storage unit 30A, a second data storage unit 30B and a third data storage unit 30C. The computing device 1000 can be a single computing device or an assembly of computing devices. The computing device 1000 can be locally arranged or remotely, such as a cloud solution. On the different data storage units 30 the different data can be stored, such as the geneticdata on the first data storage 30A, the time stamped data and / or event code data and / orphenotypic data on the second data storage 30B and privacy sensitive data, such as the connection of the before-mentioned data to an individual, on the thirds data storage 30C. Additional data storage can be also provided and / or the ones mentioned before can be combined at least in part. Another data storage (not shown) can comprise data specifying for instance, air temperature, rail temperature, position of blades, model of point machine, position of point machine and / or further railway network related information. This data can also be provided on one or more of the before-mentioned data storages. The computing unit 35 can access the first data storage unit 30A, the second data storage unit 30B and the third data storage unit 30C through the internal communication channel 160, which can comprise a bus connection 160. The computing unit 30 may be single processor or a plurality of processors, and may be, but not limited to, a CPU (central processing unit), GPU (graphical processing unit), DSP (digital signal processor), APU (accelerator processing unit), ASIC (application-specific integrated circuit), ASIP (application-specific instruction-set processor) or FPGA (fieldprogramable gate array). The first data storage unit 30A may be singular or plural, andmay be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). The second data storage unit 30B may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). The third data storage unit 30C may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). It should be understood that generally, the first data storage unit 30A (also referred to as encryption key storage unit 30A), the second data storage unit 30B (also referred to as data share storage unit 30B), and the third data storage unit 30C (also referred to as decryption key storage unit 30C) can also be part of the same memory. That is, only one general data storage unit 30 per device may be provided, which may be configured to store the respective encryption key (such that the section of the data storage unit 30 storing theencryption key may be the encryption key storage unit 30A), the respective data elementshare (such that the section of the data storage unit 30 storing the data element share may be the data share storage unit 30B), and the respective decryption key (such that the section of the data storage unit 30 storing the decryption key may be the decryption key storage unit 30A). In some embodiments, the third data storage unit 30C can be a secure memory device 30C, such as, a self-encrypted memory, hardware-based full disk encryption memory and the like which can automatically encrypt all of the stored data. The data can be decrypted from the memory component only upon successful authentication of the party requiring to access the third data storage unit 30C, wherein the party can be a user, computing device, processing unit and the like. In some embodiments, the third data storage unit 30C can only be connected to the computing unit 35 and the computing unit 35 can be configured to never output the data received from the third data storage unit 30C. This can ensure asecure storing and handling of the encryption key (i.e., private key) stored in the third datastorage unit 30C. In some embodiments, the second data storage unit 30B may not be provided but insteadthe computing device 1000 can be configured to receive a corresponding encrypted sharefrom the database 60. In some embodiments, the computing device 1000 may comprise the second data storage unit 30B and can be configured to receive a corresponding encrypted share from the database 60. The computing device 1000 may comprise a further memory component 140 which may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). The memory component 140 may also be connected with the other components of the computing device 1000 (such as the computing component 35) through the internal communication channel 160. Further the computing device 1000 may comprise an external communication component 130. The external communication component 130 can be configured to facilitate sending and / or receiving data to / from an external device (e.g., backup device 10, recovery device 20, database 60). The external communication component 130 may comprise an antenna (e.g., WIFI antenna, NFC antenna, 2G / 3G / 4G / 5G antenna and the like), USB port / plug, LAN port / plug, contact pads offering electrical connectivity and the like. The external communication component 130 can send and / or receive data based on a communication protocol which can comprise instructions for sending and / or receiving data. Said instructions can be stored in the memory component 140 and can be executed by the computing unit 35 and / or external communication component 130. The external communication component 130 can be connected to the internal communication component 160. Thus, data received by the external communication component 130 can be provided to the memory component 140, computing unit 35, first data storage unit 30A and / or second data storage unit 30B and / or third data storage unit 30C. Similarly, data stored on the memory component 140, first data storage unit 30A and / or second data storage unit 30B and / or third data storage unit 30C and / or data generated by the commuting unit 35 can be provided to the external communication component 130 for being transmitted to an external device.In addition, the computing device 1000 may comprise an input user interface 110 whichcan allow the user of the computing device 1000 to provide at least one input (e.g., instruction) to the computing device 100. For example, the input user interface 110 may comprise a button, keyboard, trackpad, mouse, touchscreen, joystick and the like. Additionally, still, the computing device 1000 may comprise an output user interface 120 which can allow the computing device 1000 to provide indications to the user. For example, the output user interface 110 may be a LED, a display, a speaker and the like. The output and the input user interface 100 may also be connected through the internal communication component 160 with the internal component of the device 100. The processor may be singular or plural, and may be, but not limited to, a CPU, GPU, DSP,APU, or FPGA. The memory may be singular or plural, and may be, but not limited to,being volatile or non-volatile, such an SDRAM, DRAM, SRAM, Flash Memory, MRAM, F-RAM, or P-RAM. The data processing device can comprise means of data processing, such as, processor units, hardware accelerators and / or microcontrollers. The data processing device 20 can comprise memory components, such as, main memory (e.g., RAM), cache memory (e.g.,SRAM) and / or secondary memory (e.g., HDD, SDD). The data processing device cancomprise busses configured to facilitate data exchange between components of the data processing device, such as, the communication between the memory components and the processing components. The data processing device can comprise network interface cards that can be configured to connect the data processing device to a network, such as, to the Internet. The data processing device can comprise user interfaces, such as:▪ output user interface, such as:o screens or monitors configured to display visual data (e.g., displaying graphical userinterfaces of railway network status), ospeakers configured to communicate audio data (e.g., playing audio data to theuser),▪ input user interface, such as:o camera configured to capture visual data (e.g., capturing images and / or videos ofthe user), omicrophone configured to capture audio data (e.g., recording audio from the user),o keyboard configured to allow the insertion of text and / or other keyboard commands(e.g., allowing the user to enter text data and / or other keyboard commands by havingthe user type on the keyboard) and / or trackpad, mouse, touchscreen, joystick – configured to facilitate the navigation through different graphical user interfaces of the questionnaire. The data processing device can be a processing unit configured to carry out instructions of a program. The data processing device can be a system-on-chip comprising processing units, memory components and busses. The data processing device can be a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer. The data processing device can be a server, either local and / or remote. The data processing device can be a processing unit or a system-on-chip that can be interfaced with a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer and / or user interface (such as the upper-mentioned user interfaces).- input user interface, such as:o camera configured to capture visual data (e.g., capturing images and / or videos ofthe user), omicrophone configured to capture audio data (e.g., recording audio from the user),o keyboard configured to allow the insertion of text and / or other keyboard commands(e.g., allowing the user to enter text data and / or other keyboard commands by having the user type on the keyboard) and / or trackpad, mouse, touchscreen, joystick – configured to facilitate the navigation through different graphical user interfaces of the questionnaire. The data processing device can be a processing unit configured to carry out instructions of a program. The data processing device can be a system-on-chip comprising processing units, memory components and busses. The data processing device can be a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer. The data processing device can be a server, either local and / or remote. The data processing device can be a processing unit or a system-on-chip that can be interfaced with a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer and / or user interface (such as the upper-mentioned user interfaces). Fig. 5 depicts a chart outputted by the present invention, representing the retrieved measurements of clinical parameters with respect to their corresponding urgency level zones. In simple words, the measurements retrieved by the invention is presented in a chart wherein the measurements are also presented in tandem with the “zone” in which it is categorized.For instance, these zones can represent the urgency level of Spontaneous Breathing Trial(SBT in Figure. 5) such as No SBT indication, SBT indication and Urgent SBT indication.It should be understood that in certain embodiments, these zones are not related to anSBT urgency level, but any urgency level that can correspond to the measurements takenby the invention, wherein said urgency levels may be traced back to the standardized database.Fig. 6 depicts chart outputted by the present invention, representing the scores calculatedby the system as well as a note 500 inputted into the system by an authorized user, with respect to time. In simple terms, an authorized user may input at least one note 500 at a certain time and date relative to the score calculated by the system at said time and date. Said note may be displayed with the scores calculated with respect to time to at least one authorized user. While in the above, a preferred embodiment has been described with reference to the accompanying drawings, the skilled person will understand that this embodiment was provided for illustrative purpose only and should by no means be construed to limit the scope of the present invention, which is defined by the claims. Fig. 7 depicts a chart outputted by the present invention, representing the retrieved measurements of clinical parameters with respect to their corresponding success and / or failure level zones. In simple words, the measurements retrieved by the invention is presented in a chart wherein the measurements are also presented in tandem with the “zone” in which it is categorized.For instance, these zones can represent the successfukness of Spontaneous Breathing Trial(SBT in Figure.7) such as successful SBT indication, continue SBT indication and SBT failure indication. It should be understood that in certain embodiments, these zones are not related to anSBT success level, but any test success level that can correspond to the measurementstaken by the invention, wherein said success levels may be traced back to the standardized database. Whenever a relative term, such as “about”, “substantially” or “approximately” is used in this specification, such a term should also be construed to also include the exact term. That is, e.g., “substantially straight” should be construed to also include “(exactly) straight”. Whenever steps were recited in the above or also in the appended claims, it should be noted that the order in which the steps are recited in this text may be accidental. That is, unless otherwise specified or unless clear to the skilled person, the order in which steps are recited may be accidental. That is, when the present document states, e.g., that a method comprises steps (A) and (B), this does not necessarily mean that step (A) precedes step (B), but it is also possible that step (A) is performed (at least partly) simultaneously with step (B) or that step (B) precedes step (A). Furthermore, when a step (X) is said to precede another step (Z), this does not imply that there is no step between steps (X) and(Z). That is, step (X) preceding step (Z) encompasses the situation that step (X) isperformed directly before step (Z), but also the situation that (X) is performed before one or more steps (Y1), …, followed by step (Z). Corresponding considerations apply when terms like “after” or “before” are used.

Claims

Claims1. A method for monitoring invasive mechanical ventilation, the method comprisingretrieving at least one data from at least one data source, preprocessing the at least one data to automatically generate at least one preprocessed data,categorizing the at least one preprocessed data into at least one invasivemechanical ventilation status data, and activating at least one artificial-intelligence-assisted module,wherein the at least one artificial-intelligence-assisted module is activated according to the at least one invasive mechanical ventilation status data.

2. The method according to the preceding method claim, wherein preprocessing stepcomprises at least one of performing at least one measurement unitconversion; discarding at least one out-of-range value; processing at least one ventilator parameter data. processing at least one blood chemistry data; and processing at least one physiological data.

3. The method according to any of the preceding method claims, wherein the at leastone artificial-intelligence assisted module comprises at least one of: a first module, a second module, a third module, a fourth module, a fifth module, a sixth module, a seventh module, and an eighth module.

4. The method according to any of the preceding method claims, wherein the methodcomprises determining which of the at least one artificial-intelligence-assisted module to activate wherein determining which at least one artificial-intelligence-assisted module to activate is based on the at least one invasive mechanicalventilation status data.

5. The method according to any of the preceding method claims, wherein the at leastone preprocessed data is classified in at least one zone wherein further the at least one zone is delimited according to at least one range of values, wherein the atleast one range of values is defined, with respect to a standardized database, to assess readiness for a Spontaneous Breathing Trial.

6. The method according to any of the preceding method claims, wherein the methodcomprises predicting at least one probability of successful weaning from invasive mechanical ventilation, wherein successful weaning from mechanical ventilation is defined as consecutive 48-hours without mechanical ventilation, wherein the predicting is based on the at least one invasive mechanical ventilation status data and / or onthe at least one preprocessed data, wherein the predicting is performed by the atleast one artificial-intelligence assisted module; predicting at least one number of hours before successful weaning from mechanical ventilation at the current time, wherein successful weaning from mechanical ventilation is defined as consecutive 48-hours without mechanical ventilation, wherein the predicting is based on the at least one invasive mechanical ventilation status data and / or on the at least one preprocessed data, wherein thepredicting is performed by the at least one artificial-intelligence assisted module; predicting at least one probability of initiating invasive mechanical ventilation, wherein the predicting is based on the at least one invasive mechanical ventilation status data and / or on the at least one preprocessed data, wherein the predictingis performed by the at least one artificial-intelligence assisted module; predicting at least one number of hours before initiating mechanical ventilation at the current time wherein the predicting is based on the at least one invasive mechanical ventilation status data and / or on the at least one preprocessed data,wherein the predicting is performed by the at least one artificial-intelligence assisted module.

7. The method according to any of the preceding method claims, wherein the methodcomprises at least one of: predicting future invasive mechanical ventilation weaning trajectory, wherein themethod comprises automatically generating at least one successful weaning probability level, wherein the at least one successful weaning probability level expresses a lack of necessity for a patient’s invasive mechanical ventilation, wherein the successful weaning probability level is based on the predicting of afuture invasive mechanical ventilation weaning trajectory, wherein the methodcomprises outputting at least one decision-supporting-data based on the predicting of the invasive mechanical ventilation weaning trajectory;predicting a future invasive mechanical ventilation necessity trajectory, whereinthe method comprises automatically generating at least one risk level, whereinthe at least one risk level expresses a necessity for a patient’s invasive mechanical ventilation, wherein the risk level is based on the predicting of a future invasive mechanical ventilation necessity trajectory, wherein the method comprisesoutputting at least one decision-supporting-data based on the predicting of the predicting of the future invasive mechanical ventilation necessity trajectory.

8. The method according to the preceding method claim and with the features ofclaim 3, wherein predicting at least one probability of successful weaning from mechanical ventilation within 24 hours at the current time is performed by the first module; predicting at least one probability of successful weaning from mechanical ventilation within 48 hours at the current time is performed by the second module; predicting at least one probability of successful weaning from mechanical ventilation within 72 hours at the current time is performed by the third module; predicting at least one number of hours before successful weaning from mechanical ventilation at the current time is performed by the fourth module; predicting at least one probability of initiating mechanical ventilation within 24 hours at the current time is performed by the fifth module; predicting at least one probability of initiating mechanical ventilation within 48 hours at the current time is performed by the sixth module; predicting at least one probability of initiating mechanical ventilation within 72 hours at the current time is performed by the seventh module; predicting at least one number of hours before initiating mechanical ventilation at the current time is performed by the eighth module;9. A system for monitoring invasive mechanical ventilation, the system comprisinga retrieving component configured to retrieve at least one data from at least one data source, a processing component configured to at least preprocess the at least one data to automatically generate at least one preprocessed data, a categorizing component configured to categorize the at least one preprocessed data into at least one invasive mechanical ventilation status data, and a activating component configured to activate at least one artificial-intelligence-assisted module, wherein the activating component is configured to activate the at least one artificial-intelligence-assisted module based on the at least one invasive mechanical ventilation status data.

10. The system according to the preceding system claim wherein the processingcomponent is configured to perform at least one measurement unit conversion, discard at least one out-of-range value,process at least one ventilator parameter data, process at least one blood chemistry data, and process at least one physiological data.

11. The system according to any preceding system claim wherein the at least oneartificial-intelligence assisted module comprises at least one of: a first module, a second module, a third module, a fourth module, a fifth module, a sixth module, a seventh module, and an eighth module.

12. The system according to any of the preceding system claims, wherein the systemis configured to determine which of the at least one artificial-intelligence-assisted module to activate depending on the at least one invasive mechanical ventilationstatus data.

13. The system according to any of the preceding system claims, wherein the systemis configured to categorize the at least one preprocessed data in at least one zonewherein further the at least one zone is delimited according to at least one range of values, wherein the at least one range of values is defined, with respect to a standardized database, to assess readiness for a Spontaneous Breathing Trial.

14. The system according to any of the preceding system claims, wherein the systemis configured to predict at least one probability of successful weaning from invasive mechanical ventilation, wherein successful weaning from mechanical ventilation is defined asconsecutive 48-hours without mechanical ventilation, wherein the system is configured to predict based on the at least one preprocessed data and / or on theat least one invasive mechanical ventilation status data, wherein the system is configured to predict by means of the at least one artificial-intelligence assisted module; predict at least one number of hours before successful weaning from mechanical ventilation at the current time, wherein successful weaning from mechanical ventilation is defined as consecutive 48-hours without mechanical ventilation, wherein the system is configured to predict based on the at least one preprocesseddata and / or on the at least one invasive mechanical ventilation status data,wherein the system is configured to predict by means of the at least one artificial- intelligence assisted module; predict at least one probability of initiating invasive mechanical ventilation, wherein the system is configured to predict based on the at least one preprocesseddata and / or on the at least one invasive mechanical ventilation status data,wherein the system is configured to predict by means of the at least one artificial- intelligence assisted module; predict at least one number of hours before initiating mechanical ventilation at the current time wherein the system is configured to predict based on the at least one preprocessed data and / or on the at least one invasive mechanical ventilationstatus data, wherein the system is configured to predict by means of the at leastone artificial-intelligence assisted module.

15. The system according to any preceding system claim wherein the system isconfigured to predict a future invasive mechanical ventilation weaning trajectory, wherein the system is configured to automatically generate at least one successful weaning probability level, wherein the at least one successful weaning probability level expresses a lack of necessity for a patient’s invasive mechanical ventilation, wherein the successful weaning probability level is based on the predicting of a future invasive mechanical ventilation weaning trajectory, wherein the system is configured to output at least one decision-supporting-data based on the predicting of the invasive mechanical ventilation weaning trajectory; predict a future invasive mechanical ventilation necessity trajectory, wherein the system is configured to automatically generate at least one risk level, wherein the at least one risk level expresses a necessity for a patient’s invasive mechanical ventilation, wherein the risk level is based on the predicting of a future invasive mechanical ventilation necessity trajectory, wherein the system is configured to output at least one decision-supporting-data based on the predicting of theinvasive mechanical ventilation necessity trajectory.

16. The system according to the preceding system claim and with the features of claim11, wherein the first module is configured to predict at least one probability of successful weaning from mechanical ventilation within 24 hours at the current time; the second module is configured to predict at least one probability of successful weaning from mechanical ventilation within 48 hours at the current time; the third module is configured to predict at least one probability of successful weaning from mechanical ventilation within 72 hours at the current time; the fourth module is configured to predict at least one number of hours before successful weaning from mechanical ventilation at the current time; the fifth module is configured to predict at least one probability of initiating mechanical ventilation within 24 hours at the current time; the sixth module is configured to predict at least one probability of initiating mechanical ventilation within 48 hours at the current time; the seventh module is configured to predict at least one probability of initiating mechanical ventilation within 72 hours at the current time; the eighth module is configured to predict at least one number of hours beforeinitiating mechanical ventilation at the current time.

17. A computer program comprising instructions which, when the program is executedby a computer, cause the computer to carry out the method according to any of the preceding method claims.

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