22 results about "Treatment intervention" patented technology

The invention relates to the technical field of intelligent medical treatment, and discloses an AI-based severe patient sepsis early warning method and system. According to the method, a multi-organ interaction mechanism is deeply analyzed by dynamically constructing an organ-level causal network, and early-stage accurate early warning of sepsis is realized: high-frequency physiological waveforms, asynchronous laboratory indexes and treatment intervention data are fused, and the capture ability of microcirculation failure and autonomic nerve decline is improved; the early warning threshold value is dynamically adjusted based on the treatment coverage degree, and the delay and false alarm defects of a fixed threshold value mechanism are effectively overcome; a pathogen targeted therapy map and an organ function support scheme are automatically generated by a graded triggered clinical action chain, and the clinical decision response time is shortened; according to the method, the risk of missed diagnosis is reduced while the early warning sensitivity is improved, and an earlier and more reliable intervention window is provided for critical patients.

The invention discloses a sepsis patient organ function damage early warning judgment method based on machine learning, and the method comprises the steps: obtaining the multi-modal clinical data of a sepsis patient, including vital sign time sequence data, inspection data, treatment intervention data and static patient basic information; after data standardization processing, organ function associated features are extracted through a multi-scale feature fusion strategy, and an organ-level feature set is constructed; the method comprises the following steps of: obtaining a multi-organ collaborative early warning model, inputting the multi-organ collaborative early warning model into a pre-trained multi-organ collaborative early warning model, respectively constructing an inter-organ compensatory relation map by double branches of the model, quantifying organ injury risk contribution degree, and outputting a multi-organ functional injury risk matrix by combining with attention mechanism weighted fusion; and generating an early warning result based on the risk matrix and the organ specificity early warning threshold, including the injury risk level of each organ and the dominant risk key feature identifier, monitoring data update in real time, and dynamically adjusting the interval to update the early warning result. According to the invention, the limitation of traditional single-source data and single-organ early warning is broken through, and the early warning accuracy and real-time performance are improved.

The invention relates to the technical field of medical information processing, and discloses an intelligent analysis system for intensive care data, comprising: S1, an intervention anchor time base construction module for constructing a unified time reference based on equipment time service information and treatment intervention records, dividing intervention windows by taking an intervention event as a center, and generating an intervention anchor identifier; and S2, a data quality and working condition identification module. Intervention anchoring time base construction, data quality and working condition recognition, disease course trajectory modeling and evidence chain generation are sequentially executed under a unified time reference, and key nodes in a disease course trajectory map are condensed into decision capsules with evidence chain identifiers through a decision capsule generation module. Windowed alignment, quality layering and intervention response modeling are carried out on multi-source asynchronous monitoring data, so that the effects of continuously outputting intelligent analysis conclusions which are controlled in number, clear in basis and capable of being directly executed by medical care under intensive care complex working conditions, improving risk identification stability and reducing ineffective early warning interference are achieved.

The invention discloses a distributed processing method for funnel chest treatment influence data in combination with an edge network, and relates to the technical field of medical information technology and edge computing intelligent processing, and the method comprises the steps: S1, collecting the dynamic image and sign data of a patient through an edge node, carrying out the preliminary classification of the collected real-time data through a distributed processing frame, and carrying out the calculation of the collected real-time data; determining a data priority sequence according to the mode applicable symptom and the historical curative effect data, and obtaining a real-time data group with the priority higher than a preset threshold value; s2, according to the real-time data groups of which the priorities are higher than a preset threshold value, executing on-site conversion operation at edge nodes, grouping converted data features by adopting a k-means clustering algorithm, and determining feature groups meeting conditions; according to the distributed processing method for the funnel chest treatment influence data in combination with the edge network, the data processing real-time performance, the treatment scheme individuation and the cross-node consistency are remarkably improved, and an efficient and accurate treatment intervention path is provided for a patient.

The invention provides a psychological health analysis and intervention method based on multi-Agent collaborative assessment, which comprises the following steps of: shunting information and dialogue contents input by a user through an initial shunting Agent module, and then performing parallel and independent multi-dimensional psychological assessment by using a plurality of different assessment Agent modules. Meanwhile, each assessment Agent module corresponds to a psychological dimension and is provided with a special vertical domain knowledge base, then, structured output results of the assessment Agent modules are received and integrated through the central coordination and treatment agent, a collaborative and unified psychological health assessment report is generated, and finally, based on the assessment report, a psychological health assessment report is established. The treatment intervention Agent module is used for generating a personalized mental health intervention scheme matched with the user, and a precise intervention scheme is provided for the user.

The application relates to the technical field of medical devices and particularly discloses a pain dynamic evaluation and treatment linkage system based on three-dimensional somatosensory interaction, which comprises the following modules: a human body modeling module for constructing a three-dimensional human body dissection model; a somatosensory interaction module for receiving pain area marking instructions and pain intensity scores; an infrared sensing module for monitoring human body infrared thermal imaging data of a patient in real time; a physiological monitoring module for monitoring heart rate variability indexes and skin electric reaction indexes of the patient based on wearable devices; a region marking module for obtaining a calibrated pain area of the patient based on the pain area marking instructions and abnormal temperature zones in the human body infrared thermal imaging data; a pain prediction module for predicting a pain intensity prediction curve in a future period based on the pain intensity scores, the heart rate variability indexes, the skin electric reaction indexes and the calibrated pain area of the patient; and a treatment linkage module for generating treatment parameter control instructions based on the pain trend prediction curve and a multi-dimensional pain characteristic vector, so that precise and dynamic treatment intervention is realized.

The present invention executes a process based on cognitive behavior therapy for chronic diseases by means of a treatment assistance application installed on a patient terminal 1 for use by a patient. As a functional configuration for executing the process, a treatment assistance device comprises: a provocation content presentation unit 111 for presenting, to the patient and on the patient terminal 1, a content that provokes a symptom or a negative feeling; a reaction information acquisition unit 112 for acquiring information indicating a reaction from the patient during or after the presentation of the provocation content, through an input operation by the patient to the patient terminal 1 or a detection process by the treatment assistance application; and an intervention process determination unit 113 for determining, on the basis of the acquired reaction information, a treatment intervention process to be executed. As a result, the need for using a VR / AR projection device as a means for presenting the provocation content is eliminated, and the need for using an electroencephalograph or a specialized medial apparatus as a means for acquiring the reaction information from the patient with respect to the presentation of the provocation content is also eliminated.

The invention discloses a treatment method for regulating hypoxic ischemic brain injury of newborn mice through Prxl2b, and relates to the technical field of hypoxic brain injury. Accurate timing of treatment intervention is achieved, an optimal treatment time window is defined by establishing an oxidative stress mechanical model, it is ensured that supplementation or activation of Prxl2b plays the maximum effect in the stage where oxidative stress is most active and strongest in destructiveness, blindness of empirical drug administration is overcome, and the treatment intervention accuracy is improved. The initial injury volume and the administration delay time are quantified into calculation parameters of a mathematical model, so that the initial load dose and the maintenance dose can be customized for each model mouse, and the treatment intensity is accurately matched with the injury severity; the curative effect is maximized; and the excessive risk is potentially reduced.

The invention discloses an ankylosing spondylitis spinal injury severity prediction model construction method and system, and the method comprises the following steps: S1, carrying out the data collection of an ankylosing spondylitis patient, the data including epidemiological data, clinical phenotypes and laboratory detection indexes; s2, layering the risk layers, and dividing a spinal injury severity high-risk group and a spinal injury severity low-risk group by taking mSASSS = 25 as a threshold value; s3, eliminating radiology dependent variables through logistic regression analysis, and screening out independent predictive factors; s4, constructing a spinal injury severity prediction column graph model based on the independent prediction factors; s5, vertically mapping the measured value of each index of the patient to a scale axis corresponding to the column diagram to obtain a single score; and after the total score of the six items is accumulated, positioning is performed on a risk coordinate axis to obtain a predicted risk probability. The method improves the disease prediction capability, is high in clinical universality, is convenient and efficient to operate, and has a treatment intervention guidance value.

This invention discloses a bed-leaving detection alarm system, including a distance detection component (2) for detecting the distance from the bed, a wearable physiological indicator detection component (3) for monitoring physiological status, and a processor (4). The processor (4) receives distance and physiological indicator signals and determines whether the physiological indicators are within a preset parameter range. When not within the preset range, the analysis unit (45) determines whether the patient can return to the bed on their own: if not, an alarm command is generated to prompt medical staff to reach the patient's location and take treatment measures; if so, an alarm is generated to prompt the patient to return to the bed immediately, and the system monitors whether the return process is smooth. This system can also automatically generate monitoring directions based on the patient's medical records and department information to achieve accurate and personalized bed-leaving early warning.

This invention relates to the field of rat coronary heart disease complicated with depression treatment and intervention technology, and discloses a rat coronary heart disease complicated with depression treatment and intervention system, including a multidimensional acquisition module and an intelligent intervention module. The system acquires experimental data, metabolomics data, and efficacy data of intervention programs from all rats through the multidimensional acquisition module, and classifies them into datasets. The intelligent intervention module calculates a disease index based on the multidimensional data, accurately quantifies the severity of symptoms, and achieves dual correlation verification between metabolic mechanisms and pathological phenotypes. The multidimensional correlation has high accuracy. The intelligent intervention module evaluates the treatment effect of each rat after receiving different intervention programs, generates a treatment index, and assesses the population coverage of each intervention program's effect, generating an effectiveness index. It determines the symptom level and efficacy level of each rat, outputs corresponding evaluation results and intervention suggestions, and optimizes the intervention program through population data feedback. The intelligent intervention treatment effect is excellent.

The invention relates to the technical field of medical instruments, and particularly discloses a pain dynamic assessment and treatment linkage system based on three-dimensional somatosensory interaction, and the system comprises a human body modeling module which constructs a three-dimensional human body anatomical model; the somatosensory interaction module receives a pain area marking instruction and a pain intensity score; the infrared sensing module monitors human body infrared thermal imaging data of a patient in real time; the physiological monitoring module monitors a heart rate variability index and a skin electric response index of the patient based on the wearable device; the region marking module obtains a calibrated pain region of the patient based on the pain region marking instruction and an abnormal temperature region in the human body infrared thermal imaging data; the pain prediction module calibrates a pain area based on a pain intensity score, a heart rate variability index and a skin electric response index of the patient, and predicts a pain intensity prediction curve in a future period; the treatment linkage module generates a treatment parameter control instruction based on the pain trend prediction curve and the multi-dimensional pain feature vector; and accurate and dynamic treatment intervention is realized.

The present application relates to the technical field of pregnancy rate prediction, and particularly relates to a model establishment method and system for predicting pregnancy rate of early-onset ovarian dysfunction, which obtains clinical data of a patient, identifies four types of prediction key points, including basic physiological characteristics, ovarian reserve function, treatment intervention and pregnancy outcome correlation. When the total amount of key points exceeds a first threshold value, a dimensionality reduction improved feedforward neural network is constructed, and basic physiological characteristics and treatment intervention data are used as inputs to predict ovarian function and pregnancy outcome. When the number of key points is small, a graph neural network is used, a directed graph structure is constructed based on clustering and hierarchical relationship of each key point, and node state is dynamically updated based on basic physiological characteristics, so as to realize classification prediction of ovarian function and pregnancy risk, improve prediction accuracy and model interpretability, and provide personalized and intelligent decision support for fertility evaluation of POI patients.

The present invention provides a method and system for displaying medical data. First-category medical data and second-category medical data of a patient are acquired, and on the basis of the medical data, summary information of one or more physiological structures of the patient is obtained. Then, the summary information of the one or more physiological structures is correspondingly displayed in one or more display areas of a display interface. The summary information of the physiological structure comprises three summaries: a summary of the overall state of the physiological structure, a summary of a treatment measure based on the details thereof, and a summary of condition based on the details of physiological data and / or changes therein; and further comprises: the details of the treatment measure related to the physiological structure, and the details of physiological data and / or changes therein related to the physiological structure. A physician can basically grasp the condition of the patient by reviewing the three summaries, which is highly convenient. In addition, the accuracy of two summaries can be verified by means of the details of the treatment measure and the details of physiological data and / or changes therein, improving the reliability of the three summaries.

A method may include receiving objective information associated with a user that uses the at least one medical device during execution of a treatment plan, and based on the objective information, generating, using one or more terms defined in at least one medical terminology database, at least one input prompt. The method may also include providing, to an at least artificial intelligence engine configured to provide one or more predictions indicating one or more treatment interventions, the at least one input prompt. The method may also include receiving at least one prediction indicating one or more treatment interventions, and controlling, while the user uses the at least one medical device, and based on at least one aspect of the one or more treatment interventions indicated by the at least one prediction, the at least one medical device.

The invention relates to the technical field of medical data processing, in particular to an acute shock early recognition and typing system based on multi-parameter fusion. Firstly, various related physical sign parameter time sequence data are acquired from the data acquisition module; the anomaly analysis module analyzes the deviation between the data value at each moment and a preset range to obtain an anomaly expression coefficient, reflect the degree of deviation from a normal range, compare the difference of the anomaly expression coefficients between the moments, determine the treatment influence expression degree and evaluate the treatment measure effect; the multi-feature fusion module analyzes the time sequence and similar features of the treatment influence expression degree, fuses the time sequence and similar features with abnormal expression coefficient difference features, calculates significance indexes, and comprehensively evaluates the importance of physical sign parameters; and finally, in a weight adjustment and neural network analysis module, adjusting a preset importance weight based on the significance index, so that the system adjusts different sign parameter weights in real time, automatically learns the feature importance degree, and improves the neural network identification and accurate typing capabilities.

The application discloses a liver care system and a whole-process management method for chronic disease prevention and control. The system comprises a perception layer, a data layer, an application layer and a display layer connected in sequence. The perception layer collects multi-dimensional health data. The data layer constructs a liver disease specialist data resource pool. The application layer realizes four core functions. The display layer supports multi-terminal visual access. The management method comprises four closed-loop stages of precise screening, diagnosis and treatment intervention, out-of-hospital management and dynamic optimization. Through hierarchical detection and artificial intelligence assisted decision-making, an individualized scheme is generated. Combined with the doctor-patient collaborative interaction mechanism, whole-process monitoring and dynamic adjustment of the scheme are realized. A simple formula is introduced to quantify the risk and intervention effect. The scheme breaks the barriers of in-hospital and out-of-hospital management, improves the precision of chronic liver disease prevention and control and the patient compliance, guarantees data security, provides support for clinical research and policy making, and is suitable for multi-scene popularization.

The invention relates to a periodic Cushing syndrome screening method. A marker of the method is annexin A1. Compared with an existing periodic Cushing syndrome identification method, the method has the advantages that only further examination and treatment measures need to be taken when cortisol exceeds normal again, so that the treatment time of a patient is remarkably shortened, the life quality of the patient is remarkably improved, medical resources are remarkably saved, missed diagnosis and misdiagnosis of the periodic Cushing syndrome are reduced, and the method is suitable for popularization and application. And the method has remarkable economic and social benefits.

The invention discloses a craniofacial skeleton digital twin growth prediction system and method, and relates to the technical field of digital medical treatment and orthodontic intelligent auxiliary decision making. The system comprises a data acquisition module for acquiring multi-modal craniomaxillofacial data and dynamic biomechanical time sequence data; the data processing module is used for preprocessing and registering the acquired data; the digital twinborn body construction module constructs an individualized three-dimensional digital twinborn body model; the mixed growth prediction module adopts a mode of coupling data driving and mechanism simulation to simulate an accumulative growth effect under the action of dynamic load circulation and generate a growth deformation prediction result; and the visualization module visually displays the deformation process of the individualized three-dimensional digital twin model on a preset time sequence based on the growth deformation prediction result. According to the method, precise individualized dynamic prediction of the craniomaxillofacial growth trajectory and quantitative prediction of the treatment intervention effect are achieved, and the scientificity of clinical diagnosis and treatment scheme design, the reasonability of treatment opportunity selection and the reliability of prognosis evaluation are improved.

Systems and methods are disclosed for providing ongoing monitoring and updating of blood volume status, where the system or method can include guidance in the form of recommendations for treatment actions or alerts about altered patient status.

This invention relates to a patient scheduling system for a hospital emergency room. An initial assessment scheduling module classifies admitted patients by urgency level. When medical resources are strained, it compares historical emergency records and simulates pre-treatment interventions based on similar cases. Simultaneously, it constructs a waiting queue containing patients at different treatment stages and predicts the impact of adding a patient to the waiting queue on the patient themselves and other patients in the queue. A secondary assessment scheduling module, after the patient receives formal intervention, schedules nurses to perform corresponding examinations and constructs a secondary examination result vector. A comprehensive assessment module combines the urgency level and the secondary examination result vector, using a pre-trained model to obtain the final patient classification, providing a basis for subsequent treatment scheduling. This invention, through pre-intervention and predictive dynamic ranking, effectively utilizes fragmented doctor time, optimizes emergency room resource scheduling, significantly shortens patient waiting time, and improves emergency efficiency and treatment success rate.