Life information processing system and life information processing method

Abstract

A life information processing system and a life information processing method, the system including a memory, a processor, and a display, the processor performing the following operations: acquiring a plurality of types of patient data of a patient; analyzing the patient data to obtain one or more analysis results; determining a patient state of the patient and a physiological cause causing the patient state according to the one or more analysis results; displaying patient state prompt information representing the patient state and displaying physiological reason prompt information representing the physiological reason. The system prompts the physiological reasons causing the patient state while outputting the patient state, can help medical staff to know the physical condition of the patient more deeply, and helps the medical staff to perform diagnosis or treatment better.

Description

Life information processing system and life information processing method

[0001] manual Technical Field

[0002] The present application relates to the field of life information technology, and more specifically to a life information processing system and a life information processing method. Background Art

[0003] With the development of monitoring technology, current monitoring equipment is able to monitor important vital signs parameters such as electrocardiogram, blood pressure, blood oxygen saturation, respiratory rate, body temperature, etc. in real time. Based on the high-precision, high-sampling rate signals of these vital signs parameters, it provides important patient information for medical clinical diagnosis and conducts supervision and alarm for each parameter.

[0004] Traditional monitoring strategies primarily focus on monitoring physiological parameters: acquiring physiological signals, extracting vital sign parameters based on these signals, and then determining whether these parameters exceed preset values. If these thresholds are exceeded, an audible, visual, and graphic alarm is issued, alerting medical staff that specific vital sign values ​​for a particular patient are exceeding the specified threshold. These analytical methods fail to effectively utilize the vast amount of vital sign parameter information provided by currently well-developed monitoring technologies, and fail to provide intuitive and convenient indicators of changes in the patient's condition. Instead, they provide only an objective description of the patient's physiological parameters, relying heavily on the physician's experience in interpreting these vital sign parameters. Physicians with limited experience can easily overlook the potential for worsening conditions implied by simultaneous changes in multiple parameters, delaying diagnosis and treatment.

[0005] Summary of the Invention

[0006] The Summary of the Invention introduces a series of simplified concepts that will be further described in the Detailed Description of the Invention. The Summary of the Invention of this application is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0007] A first aspect of an embodiment of the present application provides a life information processing system, the life information processing system comprising a memory, a processor, and a display, wherein the memory is used to store an executable program, and the processor is used to execute the executable program, so that the processor performs the following operations: obtaining multiple types of patient data of a patient;

[0008] analyzing the patient data to determine a patient state of the patient and a physiological cause of the patient state, wherein the patient state includes at least a state of the patient's physiological system, and the physiological system includes at least one of the patient's nervous system, circulatory system, and respiratory system;

[0009] The display is controlled to display patient status prompt information representing the patient status and physiological cause prompt information representing the physiological cause, wherein the patient status prompt information at least includes an overall assessment of the status of the physiological system.

[0010] A second aspect of the present application provides a method for processing life information, the method comprising:

[0011] obtaining multiple types of patient data of a patient;

[0012] Analyzing the patient data to obtain one or more analysis results;

[0013] determining a patient state of the patient and a physiological cause of the patient state based on the one or more analysis results;

[0014] Patient status prompt information representing the patient status is displayed, and physiological cause prompt information representing the physiological cause is displayed.

[0015] According to the embodiments of the present application, the life information processing system and life information processing method obtain the patient status by comprehensively analyzing multiple types of patient data, fully considering the correlation between multiple types of patient data, and can avoid problems such as false alarms and missed alarms. It can also help medical staff quickly and reliably judge the patient's condition, which is conducive to real-time grasp of the patient's health status; while outputting the patient's status, it prompts the physiological reasons that cause the patient's status, which can help medical staff understand the patient's physical condition more deeply and better diagnose or treat the patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0017] In the attached figure:

[0018] FIG1 shows a schematic block diagram of a life information processing system according to an embodiment of the present application;

[0019] FIG2 is a schematic diagram showing a display interface according to an embodiment of the present application;

[0020] FIG3 shows a schematic flow chart of a method for processing life information according to an embodiment of the present application;

[0021] FIG4 is a schematic diagram showing a display interface according to another embodiment of the present application;

[0022] FIG5 shows a schematic diagram of a display interface according to yet another embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of the present application more apparent, the following is a detailed description of example embodiments of the present application with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application described in this application, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of this application.

[0024] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present application. However, it will be apparent to those skilled in the art that the present application can be implemented without one or more of these details. In other examples, some technical features well known in the art are not described in order to avoid confusion with the present application.

[0025] It should be understood that the present application can be implemented in different forms and should not be interpreted as being limited to the embodiments set forth herein. On the contrary, providing these embodiments will make the disclosure thorough and complete and will fully convey the scope of the present application to those skilled in the art.

[0026] The terms used herein are intended solely to describe specific embodiments and are not intended to limit this application. When used herein, the singular forms "a," "an," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "comprising" and / or "including," when used in this specification, specify the presence of the specified features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items. Furthermore, this application can be implemented in a variety of different forms and is not limited to the embodiments described in this example. The following specific examples are provided to facilitate a clearer and more thorough understanding of the disclosure herein. Words indicating directions, such as "up," "down," "left," and "right," refer only to the position of the illustrated structure in the corresponding accompanying drawings. The term "display interface" may refer to an interface of a life information processing system displaying parameter waveforms and / or parameter values; alternatively, it may refer to an interface displayed after the life information processing system is powered on; alternatively, it may refer to an interface frequently used by the life information processing system.

[0027] In order to fully understand the present application, a detailed structure will be provided in the following description to illustrate the technical solution proposed by the present application. The optional embodiments of the present application are described in detail below. However, in addition to these detailed descriptions, the present application may also have other implementation methods.

[0028] At present, monitoring equipment mainly uses the following alarm methods for alarm: one is an alarm method based on single vital sign data, which alarms when it is determined that the value of a single vital sign data exceeds the alarm threshold at a preset time point or within a preset period of time; the second is an alarm method based on multiple vital sign data, which alarms when it is determined that multiple vital sign data exceed the alarm threshold at the same time; the third is a trend alarm method based on single vital sign data, which alarms when it is determined that a single vital sign data shows a downward or upward trend within a preset period of time.

[0029] However, there is usually a correlation between vital sign data and other patient data, and the patient's status is often reflected in the comprehensive performance of multiple patient data. The alarm method of existing monitoring equipment does not take this correlation into account, which is not only prone to false alarms and missed alarms. In addition, existing monitoring equipment can only provide an objective description of the patient's physiological state, and the interpretation of vital sign parameters depends largely on the doctor's experience. Doctors with relatively limited qualifications are prone to overlooking the worsening of the disease implied by the synchronous changes of multiple parameters, delaying the patient's diagnosis and treatment. Single parameter monitoring cannot effectively utilize the information of the large amount of vital sign parameters collected by the monitoring equipment, and cannot dig out the changes in the patient's status conveyed by the vital sign parameters.

[0030] In addition, although there are related methods that conduct comprehensive analysis of multiple vital sign parameters based on machine learning and other technologies, due to the current shortcomings of machine learning and other technologies themselves, they are prone to output abnormal alarms that are difficult to accept clinically. Moreover, such methods are generally highly dependent on training data. In the current situation where clinical data acquisition is difficult, it is even more difficult to conduct sufficient model training, and the results of the model output are difficult to gain the trust of clinical medical staff.

[0031] In response to the above-mentioned deficiencies of existing monitoring equipment, an embodiment of the present application provides a life information processing system for implementing status monitoring. Status monitoring refers to monitoring the patient's status and providing prompts or alarms when an abnormal patient status is detected. The patient's status is an assessment result of the patient's overall or local physiological function, reflecting the patient's overall or local health status. In addition, the life information processing system of the embodiment of the present application not only outputs the patient's status, but also provides an explanatory description of the patient's status to explain the specific reasons for the patient's current status, thereby improving the credibility of the patient's status and assisting medical staff in quickly understanding the patient's condition.

[0032] 1 , the life information processing system 100 of an embodiment of the present application includes a memory 110, a processor 120, and a display 130, wherein the memory 110 is used to store an executable program, and the processor 120 is used to execute the executable program stored in the memory 110, so that the processor 120 performs the following operations: acquiring multiple types of patient data of a patient; analyzing the patient data to obtain one or more analysis results; determining the patient status of the patient and the physiological cause causing the patient status based on the one or more analysis results; displaying patient status prompt information representing the patient status and displaying physiological cause prompt information representing the physiological cause.

[0033] The life information processing system of the embodiment of the present application obtains the patient status and the physiological causes of the patient status by comprehensively analyzing multiple types of patient data, and prompts the patient status and physiological causes. Compared with the monitoring plan based on parameter values ​​or parameter trends, the monitoring plan based on patient status fully considers the correlation between multiple patient data, which can avoid problems such as false alarms and missed alarms in the life information processing system. Medical staff can quickly and reliably judge the patient's condition based on the patient status information, which is conducive to real-time grasp of the patient's health status.

[0034] The life information processing system 100 of the present embodiment includes, but is not limited to, any one or a combination of a patient monitor, a local central station, a remote central station, a cloud service system, or a mobile terminal. The life information processing system 100 can be a portable life information processing system, a transportable life information processing system, or a mobile life information processing system.

[0035] In one embodiment, the life information processing system 100 may be a monitor, which is used to monitor a patient's monitoring parameters in real time. The monitor may include a bedside monitor, a wearable monitor, etc. The monitor may include a ventilator monitor, an anesthesia monitor, a defibrillator monitor, an intracranial pressure monitor, an electrocardiogram monitor, etc.

[0036] The life information processing system 100 may also include a central station for receiving monitoring data transmitted by patient monitors and centrally monitoring the data. The central station may include a local central station or a remote central station. The central station connects patient monitors within one or more departments via a network to achieve real-time centralized monitoring and massive data storage. For example, the central station stores monitoring data, basic patient information, medical history information, and diagnostic information, but is not limited to these.

[0037] In some embodiments, the monitor and the central station can form an interconnected platform via BeneLink to enable data communication between the monitor and the central station. For example, the central station can access monitoring data detected by the monitor. In other embodiments, the monitor and the central station can also establish a data connection via a communication unit, including but not limited to Wi-Fi, Bluetooth, or mobile communication 2G, 3G, 4G, 5G, etc.

[0038] The life information processing system 100 of the embodiment of the present application can also include other equipment besides monitoring equipment, such as image acquisition equipment, treatment and support equipment, information systems (such as CIS, HIS, handover software, decision support system, etc.), mobile terminals (such as ward rounds), etc.

[0039] The processor 120 of the life information processing system 100 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 120 is the control center of the life information processing system 100, connecting the various components of the entire life information processing system 100 using various interfaces and lines.

[0040] The memory 110 of the life information processing system 100 is used to store executable programs. Furthermore, the memory 110 may also store patient data, such as vital signs data of patients associated with the life information processing system 100. For example, the memory 110 may primarily include a program storage area and a data storage area. The program storage area may store an operating system, application programs required for various functions, and the like. Furthermore, the memory 110 may include high-speed random access memory (RAM) and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, multiple disk storage devices, flash memory devices, or other volatile solid-state storage devices.

[0041] The display 130 is used to provide a user with a visual display output. Specifically, the display 130 can be used to provide a user with a visual display interface, including but not limited to a monitoring interface, a monitoring parameter setting interface, etc. Exemplarily, the display 130 can be implemented as a touch display, or a display 130 with an input panel, that is, the display 130 can serve as an input / output device.

[0042] In some embodiments, the life information processing system 100 further includes a data acquisition unit, such as a sensor. The sensor can be used to continuously collect monitoring data of the patient. Continuous acquisition means that the sensor measures the monitoring data multiple times continuously at preset time intervals, and the preset time refers to the shortest time corresponding to the sensor returning a monitoring data. The data acquisition unit and the processor 120 can be connected via a wired communication protocol or a wireless communication protocol so that data can be exchanged between the data acquisition unit and the processor 120. Wireless communication technologies include, but are not limited to: various generations of mobile communication technologies (2G, 3G, 4G and 5G), wireless networks, Bluetooth, ZigBee, ultra-wideband UWB, NFC, etc.

[0043] Specifically, the data acquisition unit is used to collect vital sign data of the patient. In some embodiments, the data acquisition unit can be independently provided outside the life information processing system 100 and detachably connected to the life information processing system 100. The processor 120 is also used to process the vital sign data from the data acquisition unit. In some embodiments, the life information processing system 100 may not include sensors, and the life information processing system 100 may receive monitoring data collected by an external monitoring accessory via the communication unit.

[0044] The life information processing system 100 may further include a communication unit connected to the processor 120. In some embodiments, the life information processing system 100 may establish data communication with a third-party device through the communication unit. The processor 120 also controls the communication unit to obtain data from the third-party device, or sends the vital signs data collected by the data acquisition unit to the third-party device. The communication unit includes but is not limited to WiFI, Bluetooth, NFC, ZigBee, ultra-wideband UWB or 2G, 3G, 4G, 5G and other mobile communication units. In some other embodiments, the life information processing system 100 may also establish a connection with a third-party device via a cable. The third-party device includes but is not limited to medical devices such as ventilators, anesthesia machines, infusion pumps, and image acquisition devices. The third-party device may also be a cloud service system or non-medical devices such as mobile phones, tablets, and personal computers.

[0045] Compared to life information processing systems that perform parameter alarms, the life information processing system 100 of the present embodiment performs status monitoring. Compared to parameter alarms, status monitoring is more complex clinically, and vital sign data alone cannot support the determination of a patient's status. Therefore, the life information processing system 100 of the present embodiment acquires multiple types of patient data through various channels, such as a data acquisition unit and a communication unit, and performs comprehensive analysis on these multiple types of patient data, making the analyzed patient status more accurate and avoiding incorrect or undetermined patient status due to insufficient information.

[0046] In some embodiments, the life information processing system 100 further includes an alarm unit connected to the processor 120 for outputting an alarm prompt so that medical personnel can implement appropriate first aid measures. The alarm unit includes, but is not limited to, an alarm light, an alarm speaker, etc. The alarm information may also be displayed on the display 130, a flashing alarm light may be used to alert medical personnel, or the alarm information may be played through an alarm speaker.

[0047] In order to realize user interface and data exchange, in addition to the display 130, the life information processing system 100 can also include other input / output devices connected to the processor 120, including but not limited to input devices such as keyboard, mouse, touch screen, remote control, and output devices including but not limited to printer, speaker, etc.

[0048] It should be understood that Figure 1 is only an example of the components included in the life information processing system 100 and does not constitute a limitation on the life information processing system 100. The monitoring device 100 may include more or fewer components than shown in Figure 1, or a combination of certain components, or different components.

[0049] During the process of the life information processing system 100 providing patient monitoring, the processor 120 obtains multiple types of patient data to fully determine the patient's status. The patient's status can be at least one of the patient's overall status, the status of the physiological system, the status of the organ, the status of the physiological part, and the status of the tissue. Since the patient's nervous system, respiratory system, and circulatory system are of greatest clinical concern, the status of the physiological system can include the status of the nervous system, respiratory system, and circulatory system. The status of a system, organ, or tissue reflects the macroscopic status of the corresponding system, organ, or tissue.

[0050] Exemplarily, the patient data includes at least one or more vital sign data of the patient. Vital sign data includes, but is not limited to, at least one of electrocardiogram (ECG), blood pressure, pulse oximetry, respiration, body temperature, cardiac output, carbon dioxide, motion data, video data, respiratory mechanics parameters, hemodynamic parameters, oxygen metabolism parameters, EEG parameters, bispectral index, and microcirculatory parameters. The processor 120 may obtain vital sign data collected by the life information processing system 100 from a data acquisition unit, or may receive the patient's vital sign data from an external device via a communication unit.

[0051] The vital sign data acquired by the processor 120 can be high-precision, high-sampling-frequency vital sign data related to the patient monitored by the life information processing system 100, such as real-time monitoring data such as electrocardiogram (ECG), blood pressure, respiration, blood oxygenation, body temperature, and cardiac output. This type of data changes rapidly over time and is acquired frequently, and can be presented in real time on the monitoring interface of the life information processing system 100. Due to different acquisition conditions, the sampling frequency of vital sign data may vary. For example, blood pressure data is divided into non-invasive blood pressure data and invasive blood pressure data. Invasive blood pressure data is real-time monitoring data, while non-invasive blood pressure data is generally measured intermittently, for example, non-invasive blood pressure is measured every 30 minutes to obtain the patient's systolic, diastolic, and mean blood pressure data. Similar intermittent measurement data also includes body temperature, urine volume, blood sugar, and other data.

[0052] There are multiple ways for the processor 120 to obtain vital sign data. For example, one way is to obtain real-time monitoring vital sign data, and another way is to obtain vital sign data during the patient status monitoring period. In addition to the vital sign data obtained by the life information processing system 100 itself, the life information processing system 100 can also obtain other patient data from external medical devices or non-medical devices. For example, external devices include treatment equipment, examination equipment, and third-party systems, among which treatment equipment includes ventilators, anesthesia machines, infusion pumps, extracorporeal circulation equipment, etc., examination equipment includes ultrasound imaging equipment, endoscope equipment, etc., and third-party systems include PACS systems (picture archiving and communication systems), LIS systems (laboratory information systems), CIS systems (clinical information systems), etc. For example, the patient data obtained from external devices includes at least one of the following: monitoring data and / or device data collected by ventilator equipment, monitoring data and / or device data collected by anesthesia equipment, monitoring data and / or device data collected by infusion pump equipment, disease data, test data, and examination data. The life information processing system 100 can obtain the above-mentioned patient data through communication connections with external devices.

[0053] Exemplarily, medical condition data includes at least one of basic patient information, disease diagnosis data, treatment data, nursing data, and electronic medical record data. Basic patient information includes age, weight, and gender, while disease diagnosis data includes medical history, diagnostic reports, doctor's orders, and consultation conversations. The life information processing system 100 can obtain patient medical condition data through electronic medical records or receive medical condition data input by medical personnel. Test data includes at least one of routine blood test data, liver function test data, kidney function test data, thyroid test data, urine test data, immune test data, coagulation test data, blood gas test data, routine stool test data, and tumor marker test data collected by in vitro diagnostic equipment. The life information processing system 100 can obtain patient test data through the hospital laboratory information management system. Examination data includes data collected by medical imaging equipment, specifically including at least one of DR imaging data, CT imaging data, MRI imaging data, PET imaging data, ultrasound imaging data, scale data, and physical examination data. The life information processing system 100 can obtain examination data from medical imaging equipment or an image collection and communication system.

[0054] The patient data acquired by the processor 120 may be medical data within a certain time range. The time range may be a preset time range, such as 24 hours, or may be set according to a user's instructions. Specifically, patient data within a certain time range may be selected based on the timestamp of the medical data. If certain types of patient data do not exist within the time range, for example, certain laboratory test data may not have test results within 24 hours, then the most recently acquired patient data of that type may be selected for subsequent data processing.

[0055] Processor 120 can also perform preprocessing on some patient data, particularly physiological data acquired by the data acquisition device of life information processing system 100. This includes filtering, handling anomalies / null values, and data sample alignment. Data sample alignment involves interpolating intermittently measured data, such as blood pressure, to ensure that these data can be analyzed synchronously with other data.

[0056] By pre-processing the patient data, patient data that meets the quality requirements can be extracted for subsequent analysis, especially for the continuous measurement data in the patient data. Quality analysis of this data helps to filter out data that is generated by interference and has no actual clinical value. The above-mentioned patient data obtained by the life information processing system 100 can present various information about the patient, but it can also be seen from the relevant description that the patient data related to the patient is diverse, and a large part of the data changes over time. It is difficult for clinical medical staff to conduct comprehensive and efficient clinical evaluation and decision-making based on the above-mentioned patient data. Therefore, the life information processing system 100 of the embodiment of the present application automatically determines the patient status based on the patient data to make full use of the patient data and quickly extract valid information from the patient data.

[0057] Exemplarily, to determine the patient's status, the processor 120 may determine the target rules satisfied by the patient data, thereby determining the patient's status based on the correspondence between the target rules and the patient's status. Due to the large data types and data volumes of patient data, the patient data may first be analyzed to obtain one or more analysis results reflecting the characteristics of the patient data, and the target rules satisfied by the patient data may be determined based on the analysis results. The analysis results may be quantitative features or non-quantitative features extracted from the patient data. Exemplarily, the analysis results may include time information, such as the time of occurrence and duration of the analysis result. Subsequently, based on the time information of the analysis results, the analysis results within a preset time range may be extracted and compared with the preset rules.

[0058] Exemplarily, the processor 120 may analyze a single type of patient data to obtain an analysis result, or may analyze at least two types of patient data to obtain an analysis result. When analyzing at least two types of patient data, patient data with the same, similar, or adjacent time periods may be analyzed based on the time information carried in the patient data. Analyzing at least two types of patient data may include calculating the maximum, minimum, average, or correlation of the at least two types of patient data. For example, when calculating the correlation between heart rate and blood pressure, a suitable correlation calculation method such as Pearson or Kendall may be selected, and the start and end times of the heart rate data and blood pressure data may be set. The start and end times of the two may be exactly the same or may differ to a certain extent.

[0059] Exemplarily, the analysis results extracted from the patient data include at least one of the following: parameter values, events, results of secondary processing of parameter values, and results of secondary processing of events. Parameter values ​​may include parameter values ​​extracted from monitoring data collected by sensors, such as heart rate values ​​extracted from heart rate data, respiratory rate values ​​extracted from respiratory rate data, blood oxygen values ​​extracted from blood oxygen data, etc. Parameter values ​​may also include measurement values ​​extracted from imaging data, such as ejection fraction (EF) extracted from ultrasound images. Parameter values ​​may also include biochemical index values ​​extracted from biochemical test data, such as arterial oxygen partial pressure (PaO2), brain natriuretic peptide (BNP), etc. Parameter values ​​may also include the patient's height, weight, age, etc. Parameter values ​​may also include numerical codes assigned to non-quantitative values ​​(such as mental state), such as using a value of 1 to represent mental depression, etc.

[0060] The results of the secondary processing of the parameter values ​​include the calculation results obtained using mathematical methods, such as variance, mean, median, maximum, trend change characteristics, volatility measurement, stationarity description, random characteristics, morphological patterns, statistical parameters, etc. The above-mentioned trend change characteristics are characteristic values ​​that reflect the trend changes of the parameter values ​​over a period of time. The characteristic values ​​can reflect the direction of change or the speed of change of the parameter values. For example, the heart rate increases monotonically at an average rate of +0.3 times / minute within 1 hour, and the oxygenation index decreases monotonically at an average rate of -0.5 mmHg / minute within 1 hour, where the monotonous increase can be represented by a value of 1, and the monotonous decrease can be represented by a value of -1. The secondary processing of the parameter values ​​can also include further processing of at least two different processing results, such as obtaining new parameters based on the mean and standard deviation of the heart rate.

[0061] The events obtained based on the patient data include at least one of the following: over-limit alarms, abnormal events, and clinical events. Among them, the over-limit alarm is an alarm triggered by the processor 120 detecting that the parameter value exceeds the preset alarm limit, including but not limited to the heart rate over-limit alarm, blood pressure over-limit alarm, blood oxygen saturation over-limit alarm, etc.; wherein "exceeding the preset alarm limit" can be higher than the maximum alarm limit or lower than the minimum alarm limit. Abnormal events include abnormal events such as arrhythmia that are obtained based on the waveform and other characteristics of the patient data and are not over-limit alarms. Clinical events include examination events, diagnostic events, treatment events, nursing events, etc. recorded in the patient data. When an event is obtained based on the patient data, the time when the event occurred can also be recorded for subsequent analysis. Clinical events can reflect the patient's status. For example, if the patient is receiving oxygen inhalation, it means that the patient may have unstable breathing and poor oxygenation, but the condition is relatively mild and the patient can still breathe independently. If a ventilator is used to provide assisted breathing for the patient, the patient's respiratory instability is more serious, and the patient may have lost consciousness and cannot breathe on his own. The ventilation method used by the ventilator can also reflect the severity of the patient's condition to a certain extent. For example, patients using invasive ventilation may be more serious than patients using non-invasive ventilation. The use of pressor or antihypertensive drug administration treatment indicates that the patient may have unstable circulation, so drugs are used to maintain stable blood pressure. Blood supplementation treatment to maintain sufficient blood volume (including but not limited to supplementing fresh plasma and red blood cells) indicates that the patient may have unstable circulation, so blood supplementation is used to maintain stable blood pressure. Fluid replacement therapy to maintain fluid balance (including but not limited to crystalloid rehydration and colloid rehydration) indicates that the patient may have unstable circulation, so fluid replacement is used to maintain pressure balance in the body.

[0062] The results of secondary event processing include information such as the frequency of occurrence of the event, the trend of change in frequency of occurrence of the event, and duration. The frequency of an event includes, for example, the number of heart rate violations in the past four hours, the number of atrial fibrillation events in the past two hours, and the number of ventricular tachycardia events in the last 30 minutes. The trend of frequency of occurrence includes, for example, an increase or decrease in the number of heart rate violations in the past four hours compared to the number of heart rate violations in the previous four hours, an increase or decrease in the atrial fibrillation load in the past two hours compared to the previous two hours, etc. The duration includes, for example, the duration of excessively high or low intracranial pressure (ICP), the duration of excessively high or low end-tidal carbon dioxide (etCO2), and the duration of mean arterial pressure (MAP) <65 mmHg.

[0063] For example, when performing secondary processing on parameter values ​​or events, a time range must be set so that the parameter data or events within the time range are processed secondary. For example, when calculating the mean heart rate, in addition to setting the statistical calculation method used to calculate the mean, it is also necessary to set the time range and extract the heart rate values ​​within the time range for calculating the mean. The calculated mean is the analysis result corresponding to the heart rate within the time range. In other words, for each type of patient data, analysis results at different times can be obtained, and subsequent processing can be performed on analysis results at the same or similar times based on the corresponding relationship between the analysis results and time.

[0064] By integrating and extracting information contained in various patient data, a large number of analysis results are obtained. The life information processing system 100 can then match and judge the analysis results according to the rule library, thereby determining the patient's status based on the analysis results. The rule library contains a large number of preset rules, which have a pre-established correspondence with the patient's status. The preset rules can be formulated based on various methods such as guidelines, clinical consensus, and clinical research. Compared with determining patient status based on machine learning models, determining patient status based on preset rules is more accurate, the results are more controllable, and more consistent with the clinical cognition of medical staff.

[0065] Specifically, processor 120 determines, based on the one or more analysis results, one or more target rules satisfied by the one or more analysis results. Each analysis result satisfies one target rule, or multiple analysis results satisfy one target rule, or each analysis result satisfies multiple target rules, depending on a pre-defined correspondence between the target rule and the analysis results. Satisfaction of a target rule by an analysis result may mean that the analysis result fully satisfies the target rule or that the analysis result is closest to the target rule.

[0066] Illustratively, target rules related to the patient's nervous system include indicators related to cranial nerves, target rules related to the patient's circulatory system include indicators related to hemodynamics or perfusion, and target rules related to the patient's respiratory system include indicators related to oxygenation. When determining the state of the patient's nervous system, the processor 120 may select patient data related to cranial nerves, extract analysis results, and determine the indicators related to cranial nerves satisfied by the analysis results. When determining the state of the patient's circulatory system, the processor 120 may select patient data related to hemodynamics or perfusion, extract analysis results, and determine the indicators related to hemodynamics or perfusion satisfied by the analysis results. When determining the state of the patient's respiratory system, the processor 120 may select patient data related to oxygenation, extract analysis results, and determine the indicators related to oxygenation satisfied by the analysis results.

[0067] Furthermore, the processor 120 may compare one or more analysis results with one or more preset rules in the rule base, thereby determining one or more target rules satisfied by the one or more analysis results from the one or more preset rules. The one or more preset rules may be stored in the memory 110, and the processor 120 may call the preset rules from the memory 110. The one or more preset rules may also be stored in a server, and the processor 120 may call the preset rules from the server.

[0068] Each preset rule includes one or more preset conditions for one or more analysis results. Specifically, each preset rule can include a preset condition for a single analysis result or multiple preset conditions for multiple analysis results. Preset conditions can include threshold conditions, trend conditions, qualitative conditions, and the like. Each preset rule defines a corresponding analysis result. The correspondence between the preset rule and the analysis result can be selected to be included in the analysis result for comparison within the preset rule. For example, for the preset rule "Average heart rate greater than 90," the analysis result "Average heart rate" is selected for comparison. Exemplarily, each preset rule also defines a duration. For example, for the preset rule "Average heart rate greater than 90 more than 8 times in the past 4 hours," the time range "Past 4 hours" is selected, and the analysis result "Number of times average heart rate greater than 90" is selected to determine whether the average heart rate greater than 90 in the past 4 hours is greater than 8 times. If so, the preset rule is satisfied, meaning it is the target rule. Otherwise, the preset rule is not satisfied, meaning it is not the target rule. For the preset rule of "the number of ventricular tachycardia occurrences in the past 30 minutes exceeds 5 times", select the analysis result of "the number of ventricular tachycardia occurrences" within the time range of "in the past 30 minutes" to determine whether the number of ventricular tachycardia occurrences in the past 30 minutes exceeds 5 times. If it exceeds 5 times, it means that the preset rule is met, otherwise the preset rule is not met.

[0069] When the same preset rule corresponds to at least two analysis results, the at least two analysis results may be extracted from the same patient data. For example, the same preset rule may include a preset condition for the average heart rate and a preset condition for the standard deviation of the heart rate. The at least two analysis results may also be extracted from different patient data. For example, the same preset rule may include a preset condition for the average heart rate and a preset condition for the average blood oxygen level.

[0070] At least two analysis results within the same preset rule can be a combination of parameter values ​​(or the results of secondary processing of parameter values) and events (or the results of secondary processing of events). For example, at least two analysis results within the same preset rule can be a combination of parameter values ​​and clinical events, thereby reflecting the changes in a patient's condition after providing clinical treatment (e.g., ventilator-assisted breathing, medication, fluid replacement, blood transfusion, etc.), which can be used to judge the effectiveness of treatment and determine the development trend of the patient's condition. For example, preset rule 1 states that after a ventilator-assisted breathing treatment event occurs, SpO2 gradually increases, indicating improvement in the respiratory system; preset rule 2 states that after the use of analgesics, if etCO2 is too low for too long, or RR is too low for too long, or SpO2 is too low for too long, it indicates a possible analgesic overdose, causing respiratory depression, and the need to reduce the medication dosage. In some embodiments, the same preset rule can also correspond to only one analysis result. For example, when a patient is already using a ventilator to provide respiratory support, the patient can be considered to be in a very critical condition. Therefore, "using a ventilator" can be a separate preset rule, corresponding to the patient's condition being unable to breathe independently.

[0071] There are usually more than one type of patient data that changes due to changes in the patient's status. The current life information processing system may monitor the change trend of a single patient data, but does not take into account the correlation between the change trends of multiple patient data. An insignificant change trend in a single patient data may not reflect the patient's status alone, and it may not attract the attention of medical staff, so that the information carried therein cannot be effectively utilized. However, if multiple patient data show a certain specific change trend at the same time, it can more accurately reflect the patient's status. Therefore, the life information processing system 100 of the embodiment of the present application comprehensively considers multiple change trends to determine the patient's status.

[0072] Specifically, at least one preset rule incorporates multiple change trends for comprehensive consideration, and the multiple change trends include at least a first change trend of the first information extracted from the patient data and a second change trend of the second information. The first information and the second information can be parameter values ​​extracted from the patient data, the result of secondary processing of the parameter values, events extracted from the patient data, or the result of secondary processing of the events; the first information and the second information can be extracted from the same or multiple patient data, or can be extracted from different patient data. The first change trend and the second change trend can be an upward trend, a downward trend, a fluctuating trend, a sudden change trend, etc.; the first change trend and the second change trend can also be a combination of multiple change trends, such as first rising and then falling, first falling and then fluctuating, etc.

[0073] For example, taking the case where the first information is a heart rate value, the first change trend is the change trend of the heart rate value (hereinafter referred to as the heart rate change trend), and the second information is a blood oxygen value, and the second change trend is the change trend of the blood oxygen value (hereinafter referred to as the blood oxygen change trend), after extracting the heart rate value from the heart rate data, the change trend of the heart rate value within a first time range is determined, for example, determining that the heart rate value exhibits an upward trend within the first time range; and after extracting the blood oxygen value from the blood oxygen data, the change trend of the blood oxygen value within a second time range is determined, for example, determining that the blood oxygen value exhibits a downward trend within the second time range. The first time range and the second time range can be completely identical, at least partially identical, or adjacent. There are various ways to determine the change trend. For example, a linear fit can be performed on the heart rate values ​​within the first time range, and the heart rate change trend can be determined based on the slope of the fitted line. Alternatively, the heart rate values ​​within different time windows can be averaged, and the heart rate change trend can be determined by comparing the average values. The present embodiment does not limit the method for calculating the change trend.

[0074] After obtaining the heart rate change trend and the blood oxygen change trend, the heart rate change trend and the blood oxygen change trend can be compared with the preset conditions contained in the preset rules to determine whether the combination of the heart rate change trend and the blood oxygen change trend can reflect a certain patient state. Among them, the preset conditions used for comparison with the heart rate change trend and the blood oxygen change trend can be at least two preset conditions contained in the same preset rule. The preset rule at least defines a preset heart rate change trend and a preset blood oxygen change trend. Comparing the heart rate change trend and the blood oxygen change trend at least includes determining whether the actual heart rate change trend is consistent with the preset heart rate change trend, and whether the actual blood oxygen change trend is consistent with the preset blood oxygen change trend. Data such as the patient's physiological parameters rarely change alone, but mostly change in a coordinated manner. Analyzing and judging multiple change trends at the same time is more in line with physiological laws and helps to improve the accuracy of patient status identification.

[0075] For example, in a preset rule associated with a deterioration in circulatory status, the preset heart rate change trend is an upward trend, and the preset blood oxygen change trend is a downward trend. If the actual heart rate change trend is an upward trend and the actual blood oxygen change trend is a downward trend, then the preset rule is satisfied; if the actual heart rate change trend and the actual blood oxygen change trend are both upward trends or both downward trends, or if the heart rate change trend is a downward trend and the blood oxygen change trend is an upward trend, then the preset rule is not satisfied; other combinations of heart rate change trends and blood oxygen change trends may also represent other patient states. The preset rule can also define the heart rate change trend and blood oxygen change trend as trends in the same direction, then if the heart rate change trend and the blood oxygen change trend are both upward trends or both downward trends, then the preset rule is satisfied.

[0076] Furthermore, the preset condition also includes a preset temporal correlation between the heart rate change trend and the blood oxygen change trend. This temporal correlation can include the occurrence times of the heart rate change trend and the blood oxygen change trend being at least partially identical, indicating that the heart rate and blood oxygen change in a coordinated manner. Alternatively, the temporal correlation can include the time difference between the occurrence times of the heart rate change trend and the blood oxygen change trend not exceeding a preset time difference, for example, the blood oxygen change trend occurring within 5 minutes of the heart rate change trend, indicating that changes in heart rate and blood oxygen have a certain correlation. Alternatively, the temporal correlation can also include the heart rate change trend and the blood oxygen change trend occurring within the same larger time range. Optionally, considering that changes in patient data may have a certain causal relationship, the preset condition can also define a sequential relationship between the heart rate change trend and the blood oxygen change trend. For example, the blood oxygen change trend must occur before the heart rate change trend. Otherwise, even if the occurrence times of the two overlap, the preset condition is not met. Of course, the preset condition can also not limit the sequential relationship.

[0077] After determining one or more target rules satisfied by the analysis results of the patient data, the processor 120 determines one or more patient states of the patient corresponding to the target rules, which is the patient state determined by the life information processing system 100 for the currently monitored patient. The processor 120 can use the patient state corresponding to the target rule as the determination result of the patient state based on the correspondence between the pre-set target rule and the patient state. The patient state determined by the processor 120 is usually an abnormal state because the abnormal state requires more attention from medical staff; but the patient state can also be a normal state. For example, for the circulatory system, the processor 120 can determine that the patient state is a circulatory instability state according to the target rule. When the circulatory instability state is not determined, the circulatory system is defaulted to a circulatory stable state; the processor 120 can also determine that the patient state is a circulatory stable state according to some target rules.

[0078] The patient status of the embodiment of the present application can be a predicted result of the patient's future status, or it can be a judgment result of the patient's current status. The patient status such as "circulatory instability" below can refer to the patient's current circulatory instability, or it can refer to the patient's potential circulatory instability in the future. The predicted result of the patient's future status represents the development trend of the patient's status. Presenting the predicted result of the patient's future status helps medical staff intervene early to avoid or slow down the deterioration of the patient's status. Presenting the judgment result of the patient's current status also helps medical staff to carry out targeted treatment in a timely manner.

[0079] Previous monitoring equipment could only monitor a single physiological parameter, and inexperienced medical staff could not understand the reason for the alarm when the physiological parameter exceeded the limit. Moreover, for critically ill patients, frequent alarms may cause alarm fatigue for medical staff, and they may not pay attention to the deterioration of the patient's condition. In comparison, the life information processing system 100 of the embodiment of the present application can determine the patient's status based on patient data. By summarizing a variety of patient data based on the life information processing system 100, medical staff do not need to conduct subjective analysis of large amounts of data, and directly provide the most clear, concise, and most concerned information to medical staff. When the predicted results of the patient's future status are output, treatment can be quickly intercepted when there are signs of deterioration in the patient's condition, preventing the condition from worsening as early as possible.

[0080] For example, the processor 120 may determine one or more patient states corresponding to one or more target rules based on a correspondence between the preset rules and the preset patient states. The correspondence between the preset rules and the preset patient states may be stored in the memory 110. After the processor 120 determines the target rule that matches the analysis result among the preset rules, it determines the one or more patient states corresponding to the target rule based on the correspondence between the preset rules and the patient states stored in the memory 110.

[0081] Each target rule corresponds to a patient state, meaning that if a target rule is satisfied, the patient is determined to have the patient state corresponding to that target rule. Alternatively, each target rule may correspond to multiple patient states, meaning that if a target rule is satisfied, the patient is determined to have multiple patient states simultaneously. Alternatively, multiple target rules may correspond to a patient state, meaning that only if multiple target rules are satisfied simultaneously can the patient be determined to have the patient state corresponding to the multiple target rules. The patient state may include the states of multiple physiological structures of the patient. In some embodiments, the physiological structures include at least the patient's physiological system, and physiological systems include the circulatory system, respiratory system, and nervous system, which are of greatest clinical concern to medical staff. Physiological systems may also include the locomotor system, endocrine system, digestive system, urinary system, reproductive system, etc. The state of a physiological system directly reflects the overall condition of the physiological system. Because the human physiological system is composed of multiple tissues or organs, previous monitoring devices were unable to assess the overall condition of the physiological system. However, the life information processing system 100 of the present embodiment integrates patient data from multiple types and sources to achieve a comprehensive and generalized assessment of the physiological system state based on a rule base.

[0082] When the patient's physiological system includes the nervous system, the target rule includes indicators related to the patient's cranial nerves. When the patient's physiological system includes the circulatory system, the target rule includes indicators related to the patient's hemodynamics or perfusion. When the patient's physiological system includes the respiratory system, the target rule includes indicators related to the patient's oxygenation.

[0083] Furthermore, the patient's status may also include the patient's overall status, the status of their physiological systems, the status of their organs, the status of their physiological sites, and the status of their tissues. Organs include at least one of the brain, heart, lungs, liver, stomach, and kidneys. Physiological sites include the head, chest, and abdomen. Tissues include muscle tissue, neural tissue, and epithelial tissue.

[0084] The patient status can include one or more clinically defined macroscopic states of the patient as a whole, physiological systems, organs, physiological parts, and tissues, reflecting the overall physiological function of the part, such as circulatory instability and insufficient perfusion of the circulatory system. The patient status corresponding to different physiological structures can be regarded as a section summary of the physiological structure.

[0085] The patient status may specifically include the deterioration state of physiological structures such as systems and organs, such as whether the physiological structure is currently deteriorating or whether it may deteriorate in the future. The patient status may also include whether the physiological structures such as systems and organs are abnormal, the abnormality level, the criticality level, the nursing level, etc. For example, the state of the circulatory system may include abnormal circulation or normal circulation, and the abnormal circulation may further include mild abnormal circulation or severe abnormal circulation. Optionally, the patient status may also include specific diseases related to the patient as a whole, the patient's physiological system, organ, physiological part or tissue, such as acute respiratory distress syndrome (ARDS), respiratory failure, acute kidney injury (AKI), sepsis, heart failure, brain damage, etc. Exemplarily, the patient status may also include an unknown state or a suspected abnormal state. The patient status can be regarded as a comprehensive evaluation result obtained by the life information processing system 100 instead of medical staff to summarize and analyze patient data.

[0086] In some embodiments, the life information processing system 100 can determine the status of different physiological structures of the patient separately. For example, the life information processing system 100 can configure the corresponding patient data type, data analysis method and preset rules for each physiological structure. After obtaining multiple types of patient data of the patient, the patient data can be classified according to the correspondence between the physiological structure and the patient data, and then the patient data can be analyzed and judged according to the correspondence between the physiological structure and the data analysis method and the preset rules. For example, based on the correspondence between the respiratory system and the patient data, patient data related to the respiratory system can be extracted from multiple types of patient data, and relevant analysis results can be obtained according to the pre-configured data analysis method, and the target rules satisfied by these analysis results can be determined among the multiple preset rules related to the respiratory system, thereby obtaining the target rules related to the respiratory system; finally, the state of the respiratory system can be obtained according to the correspondence between the target rules and the patient state.

[0087] The life information processing system 100 can be preconfigured with multiple physiological structures, from which users can select a target physiological structure as needed, allowing them to view patient status related to a specific physiological structure in a targeted manner. The life information processing system 100 can also configure different module combination templates for different patient or user types, and automatically select a target clinical module from multiple physiological structures based on the current patient or user type, thereby intelligently presenting the patient status of interest to different types of patients or users. For example, module combination templates tailored to the patient status of craniocerebral trauma patients, elderly patients, heart failure patients, patients on extracorporeal circulation support, and patients with acute respiratory distress syndrome (ARDS) can be defined. Alternatively, module combination templates tailored to the patient status of cardiovascular physicians, respiratory therapists, and rehabilitation management personnel can be defined to focus on the patient status of these clinical staff. The life information processing system 100 can determine only the patient status corresponding to the target physiological structure, or it can determine the patient status corresponding to multiple physiological structures but only present the patient status corresponding to the target physiological structure.

[0088] In addition, the patient data type corresponding to each physiological structure can also be customized by the user, and the patient data type corresponding to each physiological structure can be determined based on the received user instructions. The user can select the target physiological structure and the patient data type corresponding to the target physiological structure based on factors such as the patient's condition (such as heart failure, injury, respiratory failure, etc.), hospital equipment, and hospital conditions. At the same time, the section combination template can not only be used by the user who customized the template, but the user can also share or publish the customized template for others to use. In actual applications, the user can easily select the section combination template currently in use from multiple section combination templates. Multiple section combination templates can be established by the current user or by other users.

[0089] In some embodiments, the processor 120 may further determine the patient's state based on a first machine learning model for determining the patient's state, wherein the first machine learning model may serve as an auxiliary tool for the preset rules to improve the accuracy of the patient's state.

[0090] Exemplarily, the processor 120 may input the patient data or the analysis results extracted from the patient data or the target rules satisfied by the analysis results into the first machine learning model, obtain the second patient state output by the first machine learning model, and obtain the final determination result of the patient state based on the second patient state output by the first machine learning model and the first patient state determined based on the target rule. The final determination result of the patient state may include the union of the first patient state and the second patient state, that is, as long as a patient state is determined based on the rule base and based on any one of the first machine learning model, the patient is considered to have the patient state; the final determination result of the patient state may also include the intersection of the first patient state and the second patient state, that is, only when a patient state is determined based on both the rule base and the first machine learning model, the patient is considered to have the patient state; or the first patient state and the second patient state may be fused based on other strategies to obtain a more accurate patient state.

[0091] Alternatively, the processor 120 may extract analysis results from the patient data based on the first machine learning model, or perform secondary processing on the analysis results extracted from the patient data based on the first machine learning model; the processor 120 may also perform secondary processing on the patient status obtained based on the target rule based on the first machine learning model, etc.

[0092] Since the training of machine learning models is completely dependent on the training database, if the training database has a small amount of data, or if there are insufficient positive features or the features are not obvious, the accuracy of the machine learning model will be reduced. In addition, the machine learning model also has problems such as unstable prediction results and unexplainable prediction results, which makes it difficult to gain the trust of medical staff. Therefore, the embodiment of the present application determines the patient status according to preset rules, and the machine learning model can serve as an auxiliary, improving the accuracy of the patient status, and can explain the reasons for the patient status based on the target rules.

[0093] Alternatively, processor 120 may independently obtain the patient status using a machine learning model. Specifically, processor 120 may input multiple different types of patient data into the machine learning model to obtain the patient status output by the machine learning model. Alternatively, processor 120 may input analysis results extracted from the patient data into the machine learning model to obtain the patient status output by the machine learning model. In other words, the input to the machine learning model may be the original patient data or analysis results extracted from the patient data. With the research and advancement of artificial intelligence technology, the accuracy of machine learning models in identifying patient status is also continuously improving.

[0094] Conventional machine learning models cannot explain the reasons for obtaining the patient's status. For this reason, the embodiment of the present application determines the target rule based on the analysis results extracted from the patient data, so that the reason why the machine learning model obtains the patient's status can be explained by the target rule. When the processor 120 controls the display 130 to display the patient's status and the target rule at the same time, the patient's status can be determined according to the target rule, or it can be determined according to the machine learning model, or it can be determined in combination with the target rule and the machine learning model. Even if the patient's status is not determined according to the target rule, the target rule is related to the patient's status to a certain extent. In some embodiments, after determining the patient's status according to the machine learning model, the target rule corresponding to the patient's status can be inferred from the patient's status and the target rule can be displayed.

[0095] After determining the patient status based on any of the above methods, the processor 120 controls the display 130 to display patient status prompt information representing the patient status; further, the processor 120 can also control the display 130 to display an explanatory description of the obtained patient status, thereby providing support for the patient status, increasing the credibility of the patient status, and better assisting users in diagnosing and treating patients.

[0096] Among them, the explanatory description of the patient status may include at least a part of one or more target rules. By presenting the patient status prompt information through the display 130, medical staff can be reminded to pay attention to the patient in time, especially to take timely countermeasures when the patient's status is abnormal; displaying the target rules related to the patient's status can inform medical staff why the life information processing system 100 has derived the currently displayed patient status, increase the credibility of the patient's status, and on the other hand, help medical staff to provide timely symptomatic treatment. The processor 120 can update the patient status displayed on the display 130 according to a preset update cycle. The preset update cycle can be preset by the life information processing system 100, or it can be input or changed by the user, which can be measured in minutes, hours, etc. The processor 120 can also continuously monitor the patient's status in the background, and control the display 130 to update the displayed patient status when it detects a change in the patient's status.

[0097] The patient status prompt information includes an overall assessment of the status of at least one physiological structure of the patient, which briefly summarizes the overall status of the entire physiological structure and provides summary information about the entire physiological structure. The physiological structure includes the patient's physiological system, physiological organ, physiological part, tissue, physiological system characteristics or physiological organ characteristics, the physiological system includes at least one of the locomotor system, nervous system, endocrine system, circulatory system, respiratory system, digestive system, urinary system and reproductive system; the physiological organ includes at least one of the brain, heart, lung, liver, stomach and kidney; the physiological part includes at least one of the head, chest and abdomen; the tissue includes at least one of muscle tissue, nervous tissue and epithelial tissue; the characteristics of the physiological system or the characteristics of the physiological organ include at least one of input and output, coagulation, nutrition, infection, blood sugar and medical events.

[0098] Exemplarily, the processor 120 can control the display 130 to display patient status prompt information through text or graphics. Optionally, text and graphics can also be used in combination. When using text, strings related to the patient's status can be pre-configured. The strings include strings representing the patient as a whole, physiological system, organ, physiological part or tissue, as well as strings representing specific conditions, such as circulatory system + possible shock / possible heart failure / possible internal bleeding, respiratory system + possible respiratory depression, nervous system + possible intracranial hemorrhage, urinary system + possible kidney failure, immune system + possible severe infection, etc. The string can be in various forms as long as it can reflect the patient's status. For example, the string used to represent heart failure can be "circulatory system possible heart failure", "patient may have heart failure", "patient is at risk of heart failure", etc. The string can be pre-set by experts for each patient's status, or adjusted using natural language processing-related methods to adapt to the current specific patient status. Exemplarily, the string can also be configured or modified by the user.

[0099] When a graphic method is used, graphics related to each patient state that can vividly represent the patient state can be pre-stored, and the graphics can be called for display after the patient state is determined. The graphics representing the patient state can correspond to the patient as a whole, physiological system, organ, physiological part or tissue, and the patient state can be presented by marking the graphics with at least one of symbol information, color information and text information located on or near the graphics. For example, displaying the graphics representing the circulatory system in red indicates that the circulatory system state is abnormal, and displaying it in green indicates that the circulatory system state is normal. The graphics used to represent the patient state can be displayed separately in the display area corresponding to each patient state, so that medical staff can view them separately. For example, the graphics used to represent the circulatory system state are displayed in the display area corresponding to the circulatory system, the graphics used to represent the respiratory system state are displayed in the display area corresponding to the respiratory system, and the graphics used to represent the nervous system state are displayed in the display area corresponding to the nervous system, etc.

[0100] In some embodiments, as shown in FIG4 , graphics representing the patient's status can also be displayed within the same human status indicator, facilitating an overview of the patient's condition for medical personnel. The human status indicator can be a full-body or half-body diagram, and can display multiple human organs or systems. For example, a graphic representing the nervous system's status can be displayed at the head position of the human status indicator, a graphic representing the circulatory system's status can be displayed at the heart position, a graphic representing the respiratory system's status can be displayed at the chest position, and a graphic representing the digestive system's status can be displayed at the abdomen position. The human status indicator can fully display multiple organs or systems, or only display organs or systems corresponding to the target physiological structure, organs or systems currently experiencing abnormal conditions, or user-selected organs or systems. Graphics representing the status of each system or organ can be graphically depicted, and the color or dynamic changes of the graphics can be used to represent the status of the corresponding system or organ. The patient's overall condition can also be represented by a human graphic, for example, using the boundaries of the human graphic to indicate overall conditions such as sepsis or systemic infection.

[0101] Exemplarily, the processor 120 may control the display 130 to differentiate and display the prediction results of the patient's future state from the judgment results of the patient's current state, so that the user can determine which patient states are the current patient states and which patient states are possible patient states in the future.

[0102] At least a portion of one or more target rules can be displayed adjacent to the corresponding patient status to reflect the correlation between the target rules and the patient status. Displaying at least a portion of one or more target rules can include displaying several target rules with higher importance among the multiple target rules. Since there may be a large number of target rules related to the patient status, selecting several target rules with higher importance for display helps medical staff focus on them, without the need for medical staff to manually screen among multiple rules. The displayed target rules can also be sorted according to their importance, so that medical staff give priority to target rules with higher importance. In some embodiments, the importance of the target rules can also be marked by color, graphics, text, etc.

[0103] Displaying at least a portion of one or more target rules may also include displaying several preset conditions with a higher degree of importance in the multiple target rules. As described above, each target rule may include one or more preset conditions, and the preset conditions that are ultimately displayed may be a portion of the conditions in each target rule, i.e., the preset conditions that medical staff are more concerned about may be selected in each rule for display, without displaying other preset conditions in the rule. For example, some target rules include preset conditions for the patient's age, weight, condition, etc. These preset conditions are related to the thresholds of the preset conditions for vital signs data. Although they have an important impact on the judgment of the patient's status, medical staff usually do not pay attention to this type of information. Therefore, when displaying the target rules, only the preset conditions for vital signs parameters may be displayed, without displaying the preset conditions for information such as age and weight. Of course, there may be many selection criteria for the preset conditions that are ultimately displayed, which may be specifically set according to clinical needs, and the embodiments of the present application do not impose any restrictions on this.

[0104] In some embodiments, the processor 120 may further determine a status level representing the severity of the patient's condition and control the display 130 to display status level prompt information representing the status level. The processor 120 may combine multiple patient conditions to determine a comprehensive status level, such as a level representing a severe deterioration of the patient's overall condition. This comprehensive status level may be used to inform medical staff which patients require the most attention. Alternatively, the processor 120 may determine a separate status level for each patient condition, such as a separate status level for the respiratory system, nervous system, and circulatory system. These separate status levels may be used to inform medical staff which aspects of the patient require the most attention.

[0105] When a patient has certain abnormal patient states, the display target rule can prompt the cause of the patient state, but for inexperienced medical staff, it is still difficult to determine the root cause of the patient state. Therefore, the embodiment of the present application also determines the physiological cause of the patient state and controls the display 130 to display physiological cause prompt information representing the physiological cause, thereby providing the user with a deeper explanatory description of the patient state. Among them, the physiological cause is the physiological cause of the current patient state determined based on biomedical principles, clinical experience, etc., which can be a certain disease or injury (i.e., cause of disease). The physiological cause can also be another aspect of the patient state. For example, the physiological cause related to the patient state of the physiological system may be the state of a tissue or organ in the physiological system, such as the deterioration of the state of a tissue or organ leading to the deterioration of the entire system; or, the physiological cause can be the state of another physiological system, such as the deterioration of the state of one physiological system leading to the deterioration of the state of another physiological system. In short, there is a causal relationship between the physiological cause and the patient state. It should be understood that the physiological cause is not necessarily detected and actually occurred, but can also be a prediction of the potential cause.

[0106] For example, the processor 120 may determine the physiological cause of the patient's condition based on a correspondence between a preset rule and a physiological cause. The memory 110 may store the correspondence between the preset rules and the preset physiological causes. After determining that the analysis result extracted from the patient data satisfies at least one target rule, the processor 120 may determine the physiological cause of the patient's condition corresponding to the at least one target rule based on the correspondence. Each preset rule may correspond to one physiological cause, or each preset rule may correspond to multiple physiological causes, or multiple preset rules may correspond to one physiological cause.

[0107] In some embodiments, the processor 120 may also determine the physiological cause of the patient's condition based on a second machine learning model for determining the physiological cause. Specifically, the patient data, one or more analysis results extracted from the patient data, or the target rules satisfied by the analysis results may be input into the second machine learning model to obtain the physiological cause output by the second machine learning model.

[0108] In order to reflect the correlation between the physiological cause and the target rule, the processor 120 can control the display 130 to display at least a part of the target rule in association with the physiological cause prompt information. The associated display can include presenting the correlation between the physiological cause and the target rule through specific characters, or the associated display can include displaying the associated target rule and the physiological cause adjacent to each other or in the same area. It should be understood that the physiological cause itself does not have to be presented, and other information that directly or indirectly indicates the physiological cause can also be presented; or, when the physiological cause is a prediction of a potential cause, prompt information can be presented to prompt the medical staff to pay attention to certain matters, or prompt information can be presented to prompt the medical staff to pay attention to at least one of the patient's overall condition, the condition of the physiological system, the condition of the organ, the condition of the physiological part, and the condition of the tissue; or prompt information can be presented to prompt the medical staff to pay attention to one or more of the patient's overall condition, a physiological system, an organ, a physiological part, and a tissue.

[0109] For example, referring to FIG2 , based on the patient data and a plurality of preset rules, the processor 120 determines that the target rules satisfied by the analysis results obtained from the patient data include the following: Target Rule 1. MAP is between 52 and 94 mmHg and is less than 65 mmHg 22% of the time; Target Rule 2. Sl>0.7, MSl>0.9, and ASl>47; Target Rule 3. HR increases and SpO2 decreases.

[0110] Based on the correspondence between the target rules and the patient's condition, processor 120 determines that the patient's circulatory system is unstable. Based on the correspondence between the target rules and physiological causes, processor 120 determines that the physiological cause corresponding to target rule 1 is the risk of prolonged hypoperfusion; the physiological cause corresponding to target rule 2 is the potential for hypoperfusion; and the physiological cause corresponding to target rule 3 is insufficient oxygenation. Based on this information, processor 120 controls display 130 to display prompt information indicating the physiological cause of circulatory instability; displays target rules 1, 2, and 3; and displays the physiological cause corresponding to each target rule after each target rule.

[0111] In the above description, the processor 120 controls the display to display the target rule to prompt the reason for obtaining the patient's status. In addition, the processor 120 can also control the display 130 to display at least a part of the information extracted from the patient data used to obtain the patient's status, to prompt the medical staff to obtain the basis for the patient's status. For example, when the patient's status is determined based on the first machine learning model, the key information extracted from the patient data used by the machine learning model to obtain the patient's status can be determined and displayed. For example, the first machine learning model can output the key information that contributes most to the patient's status while outputting the patient's status. Displaying this key information can explain why the first machine learning model obtains the patient's status and increase the credibility of the patient's status. Optionally, the machine learning model can also output the weight of the contribution made by each key information, and the display 130 can display each key information in order of weight from high to low.

[0112] Exemplarily, the information extracted from the patient data may include the parameter values ​​extracted from the patient data, the results of secondary processing of the parameter values, events, and the results of secondary processing of the events as described above; in addition, the information extracted from the patient data may also be used to obtain a certain waveform graph or trend graph based on the patient's status, and its presentation form may be directly presenting the waveform graph, trend graph, other statistical graphs, etc., and it is not necessary to present the information extracted from the patient data in the form of characters.

[0113] A waveform graph reflects the temporal evolution of patient data over one or more cycles. For example, changes in body surface electrical potential are recorded in chronological order according to cardiac activity, forming a continuous curve known as an ECG waveform graph. Each cardiac cycle can display a corresponding set of waveforms on the ECG waveform graph. Similar waveforms include blood oxygen saturation waveforms and end-tidal carbon dioxide waveforms. A trend graph reflects the temporal evolution of one or more patient data points, and has a longer time dimension than a waveform graph. The vertical axis of a trend graph can be either an absolute value collected at a specific sampling rate or an average value calculated over fixed time periods at a specific sampling rate. A trend graph can be a curve graph, histogram, bar graph, boxplot, scatter plot, or line graph, or a combination of these.

[0114] In some embodiments, trend graphs can be used to reflect the temporal trends of events identified from patient data, such as the occurrence pattern, development trend, incidence rate, and trend of events over time. "Incidence rate" can be the frequency of an event within a minimum unit of time, while occurrence pattern, development trend, and the like are not limited to frequency. For example, they can be labeled based on the actual occurrence of the event. For example, taking atrial fibrillation as an example, each occurrence of atrial fibrillation is labeled with a special graphic / symbol. For continuous and prolonged atrial fibrillation events, the duration of the atrial fibrillation event can be reflected by the length of the horizontal axis of the graphic / symbol. The same trend graph can also be used to present changes over time in at least two types of patient data. For example, at least two of the following can be displayed simultaneously: a heart rate trend graph, a pulse rate trend graph, a blood oxygen trend graph, a non-invasive blood pressure trend graph, an invasive blood pressure trend graph, a respiration trend graph, a body temperature trend graph, a stroke volume trend graph, a cardiac output trend graph, an electrocardiogram ST segment, an electrocardiogram QT interval, blood glucose, cerebral oxygen, and urine volume, i.e., sharing the same time axis or displaying them with the time axes aligned. Simultaneously displaying trend graphs for at least two types of patient data helps to jointly present the correlation between changes in different patient data during the monitoring period. In some embodiments, the processor 120 can also perform noise evaluation on the patient data to confirm the data quality of the patient data. The noise evaluation can help determine whether the currently obtained patient status is reliable. For example, when the data quality of certain patient data is too poor, the patient status is not determined based on the patient data to prevent the output of unreliable information from interfering with the doctor's normal clinical diagnosis and treatment; or, data quality parameters can be presented while displaying the patient status. The data quality parameters reflect the data quality of the patient data used to obtain the patient status to assist medical staff in confirming the credibility of the currently displayed patient status.

[0115] As shown in FIG5 , the patient status, target rules, and physiological reasons of the embodiment of the present application can be displayed in a patient status window 510. The patient status window 510 can be a window superimposed on the conventional monitoring interface. The user can choose whether to display the patient status window, or the patient status window can automatically pop up when an abnormality occurs in the patient status. Alternatively, the patient status, target rules, and physiological reasons can be displayed in the patient status area of ​​the conventional monitoring interface, that is, a fixed display area is allocated for the patient status, target rules, and physiological reasons in the conventional monitoring interface. The conventional monitoring interface displays real-time monitoring values, waveforms, parameter alarm information, and other conventional vital signs data.

[0116] Optionally, as shown in FIG4 , the patient status, target rules, and physiological reasons information described above may also be displayed in a patient status monitoring interface. The patient status monitoring interface displays information about multiple physiological structures of the patient, with information about at least one physiological structure including summary information 410 corresponding to the physiological structure. The user may switch between the conventional monitoring interface and the patient status monitoring interface. For example, the user may click a control on the conventional monitoring interface of FIG5 to enter the patient status monitoring interface of FIG4 ; or the patient status monitoring interface may be directly displayed on the display. In some embodiments, the above information may also be provided to the physician by outputting an electronic report or printing a paper report using a printing device.

[0117] For example, as shown in FIG4 , in the patient status monitoring interface, information about different physiological structures can be displayed in different display areas. For example, each physiological structure corresponds to an independent card, or information about different physiological structures can be distinguished by different colors or boundaries. The display area corresponding to at least one physiological structure displays summary information about the physiological structure.

[0118] Exemplarily, the information extracted from patient data displayed in the display area corresponding to the respiratory system includes at least one of the following: oxygenation index (PaO2 / FiO2), blood oxygen saturation (SpO2), respiratory rate (RR), inspired oxygen concentration (FiO2), end-tidal carbon dioxide, blood gas analysis parameters, and parameters of the ventilator or oxygen therapy device. Among them, the measured values ​​of blood gas analysis parameters include lactate (Lac), arterial oxygen partial pressure (PaO2), arterial carbon dioxide partial pressure (PaCO2), etc.; the measured values ​​of ventilator parameters include tidal volume (Tv), positive end-expiratory pressure (PEEP), and the current patient oxygen delivery mode, such as whether SIMV ventilation mode is used, intubation or mask oxygen delivery, etc. The display interface can display the measured values ​​of the parameters and / or parameter change trend charts. For measured values ​​that exceed the normal range, they can be marked and highlighted. Exemplarily, the changes between these parameters and the last measured parameters can also be provided. For parameters that only display the measured values, the values ​​and change trend charts within a preset time period can also be displayed in response to the selection instruction of the parameter measurement value.

[0119] The display area corresponding to the respiratory system also displays summary information 410 of the respiratory system. The summary information of the respiratory system may include a general summary of the overall state of the respiratory system (e.g., unstable breathing), current problems with the respiratory system (e.g., transient hypoxemia, intermittent decreased compliance), suggestions to the user (e.g., please consider adjusting the ventilation support mode or parameters), possible risks (such as the risk of pressure injury), etc. The information extracted from the data related to the respiratory system displayed on the display interface may be the same as or different from the information used to obtain the state of the respiratory system. The target rules used to obtain the state of the respiratory system mainly include indicators related to the patient's oxygenation.

[0120] For example, an entry for a manual clinical assessment tool related to the respiratory system is also displayed in the display area corresponding to the respiratory system, and the user can select the entry to enable the manual clinical assessment tool.

[0121] The information displayed in the display area corresponding to the circulatory system primarily includes information extracted from hemodynamic and perfusion data. Patient data related to the circulatory system includes shock index, blood pressure, cardiac output, lactate (Lac), as well as laboratory indicators and hemodynamic parameters related to hemodynamics and perfusion. Blood pressure can be invasive or non-invasive. Laboratory indicators include, but are not limited to, hemoglobin (Hb or HGB), red blood cell count (RBC), pH, HCO₃, and base excess (BE); hemodynamic parameters include, but are not limited to, central venous pressure (CVP), peripheral vascular resistance index (SVRI), pulmonary water index (ELWI), and central venous oxygen saturation (ScvO₂). Furthermore, patient data related to the circulatory system also includes information on support devices or treatment devices related to the circulatory system, specifically including the treatment mode used and key device parameters. For example, whether extracorporeal circulation support devices such as ECMO are used; whether an intubation balloon pump (IABP) is used; and whether vasoactive medications are used.

[0122] The circulatory system display area also displays the circulatory system status, such as circulatory instability and hypoperfusion. The information extracted from the circulatory system data displayed on the display interface can be the same as or different from the information used to determine the circulatory system status. The target rules used to determine the circulatory system status primarily include indicators related to hemodynamics or perfusion.

[0123] The information displayed in the display area corresponding to the nervous system mainly includes information extracted from the patient data related to the cranial nerves. For example, the patient data related to the nervous system includes consciousness score, brain blood pressure and blood oxygen index, clinical assessment results related to the nervous system, etc. The commonly used consciousness score in clinical practice is the GCS score (Glasgow Coma Scale), but users are also allowed to define their own consciousness scoring rules. Clinical assessment results related to the nervous system include pupil size assessment results, pupil light reflex assessment results, limb muscle strength assessment results, etc.

[0124] The display area corresponding to the nervous system also displays the nervous system status. The information extracted from the nervous system-related data displayed on the display interface can be the same as or different from the information used to obtain the nervous system status. The target rules for the nervous system status mainly include indicators related to the patient's cranial nerves.

[0125] The information displayed in the display area corresponding to the heart mainly includes cardiac risk assessment results, such as the TIMI (Thrombolysis in Myocardial Infarction) score. If the medical subject has undergone a GRACE (Global Registry for Acute Coronary Syndromes) assessment, the cardiac risk assessment results may also include the GRACE score. In addition, the information displayed in the display area corresponding to the heart also includes heart rate and cardiac-related biochemical indicators, such as creatine kinase isoenzyme (CK-MB), troponin (cTn), natriuretic peptide (NT-proBNP), etc.; as well as cardiac-related alarm events, such as ST-segment elevation or depression events. For serious arrhythmia events, fatal arrhythmia event information can be displayed, including the number of arrhythmia occurrences in the past period of time.

[0126] The information displayed in the display area corresponding to the liver mainly includes liver function assessment indicators, such as alanine aminotransferase (ALT), gamma-glutamyl transaminase (GGT), total bilirubin (Tbil), direct bilirubin (Dbil), and blood ammonia (AMM). For indicators that have been evaluated multiple times, the change information between the latest indicator and the previous indicator is presented. Users can customize the liver function assessment indicators they want to view. For example, if the medical subject undergoes a liver function assessment, such as a Child-Pugh grading score, the score result information is provided in the display area corresponding to the liver section.

[0127] The information displayed in the display area corresponding to the kidneys primarily includes urine volume and fluid intake and output. Fluid intake and output include both fluid inflow and fluid output. Fluid inflow includes the total 24-hour intake and the amount of fluid pumped into the body by the infusion pump within 24 hours. Furthermore, fluid inflow can also include dietary fluid volume, etc. Fluid output includes 24-hour urine volume, 24-hour drainage volume, and fluid dehydration from other equipment. Fluid output can also include the amount of fluid produced through sweating, excretion, vomiting, and bleeding.

[0128] Exemplarily, continuing to refer to FIG5 , a human body status indicator diagram is also displayed in the patient status monitoring interface, and the human body status indicator diagram can provide an overview of the patient's status. The human body status indicator diagram can be displayed in the center of the patient status monitoring interface, but is not limited thereto. The human body status indicator diagram can fully display multiple organs or systems, or it can only display the organs or systems corresponding to the target physiological structure, the organs or systems that are currently in an abnormal state, or the organs or systems selected by the user. The graphics used to represent the status of each system or organ can be a graphic drawing of the corresponding system or organ, and the color or dynamic changes of the graphic can be used to present the status of the corresponding system or organ. The overall status of the patient can also be represented by a human body graphic. For example, the boundary line of the human body graphic can be used to represent the overall status of sepsis or systemic infection.

[0129] In summary, the life information processing system 100 of the embodiment of the present application outputs the patient's status and prompts the physiological reasons causing the patient's status, which can help medical staff to understand the patient's physical condition more deeply and help medical staff to better diagnose or treat.

[0130] Next, a method for processing life information according to an embodiment of the present application will be described with reference to Figure 3. Figure 3 is a schematic flow chart of a method 300 for processing life information according to an embodiment of the present application.

[0131] As shown in FIG3 , the life information processing method 300 of the embodiment of the present application includes the following steps:

[0132] In step S310 , multiple types of patient data of a patient are obtained;

[0133] In step S320, the patient data is analyzed to obtain one or more analysis results;

[0134] In step S330, the patient state and the physiological cause causing the patient state are determined based on the one or more analysis results;

[0135] In step S340, patient status prompt information representing the patient status and physiological cause prompt information representing the physiological cause are displayed.

[0136] Illustratively, determining a physiological cause of a patient's condition based on one or more analysis results includes: determining one or more target rules satisfied by the one or more analysis results; and determining a physiological cause corresponding to the one or more target rules. Determining a patient's condition based on one or more analysis results includes: determining one or more target rules satisfied by the one or more analysis results; and determining a patient's condition corresponding to the one or more target rules.

[0137] Exemplarily, the patient state includes at least one of the patient's overall state, the state of a physiological system, the state of an organ, the state of a physiological part, and the state of a tissue. The physiological system includes at least one of the patient's nervous system, circulatory system, and respiratory system. When the physiological system includes the patient's nervous system, the target rule includes an indicator related to cranial nerves; when the physiological system includes the patient's circulatory system, the target rule includes an indicator related to hemodynamics or perfusion; and when the physiological system includes the patient's respiratory system, the target rule includes an indicator related to oxygenation.

[0138] The life information processing method 300 of this embodiment can be implemented in the life information processing system 100 described above. Specifically, the steps of the life information processing method 300 can be executed by the processor 120 of the life information processing system 100. For details of the life information processing method 300, refer to the above description of the life information processing system 100 and are not further elaborated here.

[0139] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely illustrative and are not intended to limit the scope of the present application. Various changes and modifications may be made therein by those skilled in the art without departing from the scope and spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as required by the appended claims.

[0140] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0141] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units described is merely a logical function division. In actual implementation, other division methods may be used, such as combining or integrating multiple units or components into another device, or ignoring or not performing some features.

[0142] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0143] Similarly, it should be understood that in order to streamline the present application and aid in understanding one or more of the various inventive aspects, in the description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this approach of the present application should not be interpreted as reflecting the intention that the application claimed for protection requires more features than those explicitly recited in each claim. More precisely, as reflected in the corresponding claims, the inventive point is that the corresponding technical problem can be solved with fewer features than all the features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into the detailed description, with each claim itself serving as a separate embodiment of the present application.

[0144] It will be understood by those skilled in the art that, except where mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or apparatus disclosed herein may be combined in any combination. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature providing the same, equivalent, or similar purpose.

[0145] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of this application and to form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

[0146] The various component embodiments of the present application can be implemented in hardware, or in a software module running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some modules according to the embodiments of the present application. The application can also be implemented as a part or all of a device program (e.g., a computer program and a computer program product) for performing the method described herein. Such a program implementing the present application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0147] It should be noted that the above embodiments illustrate rather than limit the present application, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbols placed between brackets should not be construed as limiting the claims. The present application may be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names.

[0148] The above description is merely a specific embodiment or illustration of a specific embodiment of the present application, and the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. The scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A life information processing system, characterized in that: The life information processing system includes a memory, a processor, and a display, wherein the memory is used to store an executable program, and the processor is used to execute the executable program, so that the processor performs the following operations: obtaining patient data from patients; analyzing the patient data to determine a patient state of the patient and a physiological cause of the patient state, wherein the patient state includes at least a state of the patient's physiological system, and the physiological system includes at least one of the patient's nervous system, circulatory system, and respiratory system; The display is controlled to display patient status prompt information representing the patient status and physiological cause prompt information representing the physiological cause, wherein the patient status prompt information at least includes an overall assessment of the status of the physiological system.

2. The life information processing system according to claim 1, characterized in that: Analyzing the patient data to determine a physiological cause of the patient condition, including: analyzing the patient data to determine one or more target rules satisfied by the patient data; determining the physiological cause corresponding to the one or more target rules; and wherein, When the physiological system includes the patient's nervous system, the one or more target rules include indicators related to cranial nerves, When the physiological system comprises the patient's circulatory system, the one or more target rules comprise indicators related to hemodynamics or perfusion, When the physiological system comprises a respiratory system of the patient, the one or more target rules comprise an oxygenation-related metric.

3. The life information processing system according to claim 2, characterized in that: The memory is further configured to store a plurality of preset rules, and store corresponding relationships between the plurality of preset rules and preset physiological causes; Determining the physiological cause corresponding to the one or more target rules includes: According to the correspondence between the plurality of preset rules and preset physiological causes stored in the memory, the physiological cause causing the patient's state corresponding to the one or more target rules is determined.

4. The life information processing system according to any one of claims 1 to 3, characterized in that: Analyzing the patient data to determine the patient status of the patient includes: analyzing the patient data to determine one or more target rules satisfied by the patient data; determining the patient state corresponding to the one or more target rules; and wherein, When the physiological system includes the patient's nervous system, the one or more target rules include indicators related to cranial nerves, When the physiological system comprises the patient's circulatory system, the one or more target rules comprise indicators related to hemodynamics or perfusion, When the physiological system comprises a respiratory system of the patient, the one or more target rules comprise an oxygenation-related metric.

5. The life information processing system according to claim 4, characterized in that: The memory is further used to store a plurality of preset rules, and store the corresponding relationship between the plurality of preset rules and preset patient states; Determining the patient state corresponding to the one or more target rules includes: The patient state corresponding to the one or more target rules is determined according to the correspondence between the plurality of preset rules and preset patient states stored in the memory.

6. The life information processing system according to any one of claims 2 to 5, characterized in that: Analyzing the patient data to determine one or more target rules satisfied by the patient data includes: Analyzing the patient data to obtain one or more analysis results; The one or more target rules satisfied by the one or more analysis results are determined.

7. The life information processing system according to claim 6, characterized in that: The analysis result includes at least one of the following: a parameter value, an event, a result of secondary processing of the parameter value, and a result of secondary processing of the event.

8. The life information processing system according to claim 7, characterized in that: The event includes at least one of the following: an over-limit alarm, an abnormal event, or a clinical event.

9. The life information processing system according to any one of claims 2 to 5, characterized in that: The display of the physiological cause prompt information representing the physiological cause includes: The physiological cause prompt information is associated with at least a portion of the target rule and displayed.

10. The life information processing system according to claim 1, characterized in that: Analyzing the patient data to determine a physiological cause of the patient condition, including: A physiological cause of the patient state is determined based on a first machine learning model for determining a physiological cause of the patient state.

11. The life information processing system according to claim 1, characterized in that: Analyzing the patient data to determine the patient status of the patient includes: A patient state of the patient is determined based on a second machine learning model for determining a patient state.

12. The life information processing system according to any one of claims 1 to 11, characterized in that: The display of the physiological cause prompt information representing the physiological cause includes: When the number of the physiological causes is at least two, the physiological cause prompt information corresponding to each physiological cause is displayed in order of importance of the physiological causes.

13. The life information processing system according to any one of claims 1 to 12, characterized in that: The physiological system also includes at least one of the patient's motor system, endocrine system, digestive system, urinary system, and reproductive system.

14. The life information processing system according to any one of claims 1 to 13, characterized in that: The patient status also includes at least one of the patient's overall status, the status of an organ, the status of a physiological part, and the status of a tissue.

15. The life information processing system according to claim 14, characterized in that: The organ includes at least one of a brain, a heart, a lung, a liver, a stomach, and a kidney.

16. The life information processing system according to any one of claims 1 to 15, characterized in that: The patient data includes the patient's vital signs data, and the vital signs data includes at least one of electrocardiogram, blood pressure, pulse oximetry, respiration, body temperature, cardiac output, carbon dioxide, movement data, video data, respiratory mechanics parameters, hemodynamic parameters, oxygen metabolism parameters, EEG parameters, bispectral index and microcirculation parameters.

17. The life information processing system according to any one of claims 1 to 16, characterized in that: The patient data includes at least one of the following: monitoring data and / or equipment data collected by a ventilator, monitoring data and / or equipment data collected by an anesthesia machine, monitoring data and / or equipment data collected by an infusion pump, medical condition data, test data, and examination data.

18. The life information processing system according to claim 17, characterized in that: The condition data includes at least one of patient basic information, disease diagnosis data, treatment data, nursing data and electronic medical record data; The test data includes at least one of routine blood test data, liver function test data, kidney function test data, thyroid test data, urine test data, immune test data, coagulation test data, blood gas test data, routine stool test data, and tumor marker test data; The examination data includes at least one of DR image data, CT image data, MRI image data, PET image data, ultrasound image data, scale data, and physical examination data.

19. The life information processing system according to any one of claims 1 to 18, characterized in that: The patient status is a prediction result of the patient's future status and / or a judgment result of the patient's current status.

20. A method for processing life information, characterized in that: The method comprises: obtaining multiple types of patient data of a patient; Analyzing the patient data to obtain one or more analysis results; determining a patient state of the patient and a physiological cause of the patient state based on the one or more analysis results; Patient status prompt information representing the patient status is displayed, and physiological cause prompt information representing the physiological cause is displayed.

21. The life information processing method according to claim 20, characterized in that: Determining a physiological cause of the patient condition based on the one or more analysis results includes: determining one or more target rules satisfied by the one or more analysis results; The physiological cause corresponding to the one or more target rules is determined.

22. The life information processing method according to claim 20 or 21, characterized in that: Determining the patient status based on the one or more analysis results includes: determining one or more target rules satisfied by the one or more analysis results; The patient state corresponding to the one or more target rules is determined.

23. The life information processing method according to any one of claims 20 to 22, characterized in that: The patient state includes at least one of the patient's overall state, the state of a physiological system, the state of an organ, the state of a physiological part, and the state of a tissue.

24. The life information processing method according to claim 21 or 22, characterized in that: The patient state includes a state of the patient's physiological system, the physiological system including at least one of the patient's nervous system, circulatory system, and respiratory system; When the physiological system includes the patient's nervous system, the one or more target rules include indicators related to cranial nerves, When the physiological system comprises the patient's circulatory system, the one or more target rules comprise indicators related to hemodynamics or perfusion, When the physiological system comprises a respiratory system of the patient, the one or more target rules comprise an oxygenation-related metric.

25. A life information processing system, characterized in that: The monitoring device includes: a data acquisition unit, configured to acquire multiple types of patient data of a monitored subject, wherein the patient data at least includes vital sign data of the monitored subject; a memory for storing the patient data; A processor for performing the following steps: Analyzing the patient data to determine the patient status of the monitored subject and the cause of the patient status; The display is used to display patient status prompt information representing the patient status and cause prompt information representing the cause of the disease.

26. The life information processing system according to claim 25, characterized in that: The analyzing of the patient data to determine the patient status of the monitored subject and the cause of the patient status specifically includes: Analyzing the patient data to obtain one or more analysis results; determining a patient status of the monitored subject based on the one or more analysis results; A condition causing the patient condition is determined based on the one or more analysis results.

27. The life information processing system according to claim 25 or 26, characterized in that: The memory is further configured to store a plurality of preset rules, and store corresponding relationships between the plurality of preset rules and preset causes of disease; Determining the cause of the patient condition based on the one or more analysis results includes: Comparing the one or more analysis results with the plurality of preset rules, and determining one or more target rules satisfied by or closest to the one or more analysis results among the plurality of preset rules; According to the correspondence between the plurality of preset rules and preset causes stored in the memory, the cause of the patient's condition corresponding to the one or more target rules is determined.

28. The life information processing system according to any one of claims 25 to 27, characterized in that: Determining the cause of the patient condition based on the one or more analysis results includes: The one or more analysis results are input into a first machine learning model for determining the cause of the disease, and the cause of the patient condition is determined based on the first machine learning model.

29. The life information processing system according to any one of claims 25 to 28, characterized in that: The memory is further used to store a plurality of preset rules, and store the corresponding relationship between the preset rules and the preset patient status; Determining the patient status of the monitored subject based on the one or more analysis results includes: Comparing the one or more analysis results with the plurality of preset rules, and determining at least one target rule satisfied by the one or more analysis results among the plurality of preset rules; The patient state of the monitored object is determined according to the preset patient state corresponding to the at least one target rule.

30. The life information processing system according to any one of claims 25 to 29, characterized in that: Determining the patient status of the monitored subject based on the one or more analysis results includes: The one or more analysis results are input into a second machine learning model for determining a patient state, and the patient state of the monitored object is determined based on the second machine learning model.

31. The life information processing system according to any one of claims 25 to 30, characterized in that: The patient status includes at least one of the following: the overall status of the patient, the status of the physiological system, the status of the organ, the status of the physiological part, and the status of the tissue.

32. The life information processing system according to claim 31, characterized in that: The physiological system includes at least one of the musculoskeletal system, nervous system, endocrine system, circulatory system, respiratory system, digestive system, urinary system and reproductive system; and / or the organ includes at least one of the brain, heart, lungs, liver, stomach and kidneys.

33. The life information processing system according to any one of claims 33 to 47, characterized in that: The displaying of the patient status prompt information representing the patient status includes: presenting the patient status through text or graphics.

34. The life information processing system according to claim 33, characterized in that: The graph corresponds to one or more of the patient's overall state, the state of a physiological system, the state of an organ, the state of a physiological part, and the state of a tissue.

35. The life information processing system according to claim 33 or 34, characterized in that: The displaying of the patient status includes: marking the graphic to present the patient status by at least one of symbol information, color information, and text information located on or near the graphic.

36. The life information processing system according to any one of claims 25 to 34, characterized in that: The patient data includes the patient's vital signs data, and the vital signs data includes at least one of electrocardiogram, blood pressure, pulse oximetry, respiration, body temperature, cardiac output, carbon dioxide, movement data, video data, respiratory mechanics parameters, hemodynamic parameters, oxygen metabolism parameters, EEG parameters, bispectral index and microcirculation parameters.

37. The life information processing system according to any one of claims 25 to 36, characterized in that: The patient data includes at least one of the following: monitoring data and / or equipment data collected by a ventilator, monitoring data and / or equipment data collected by an anesthesia machine, monitoring data and / or equipment data collected by an infusion pump, medical condition data, test data, and examination data.

38. The life information processing system according to claim 37, characterized in that: The condition data includes at least one of the following: basic patient information, disease diagnosis data, treatment data, nursing data, and electronic medical record data; and / or, The test data include blood test data, liver function test data, kidney function test data, thyroid test data, urine test data, immune test data, coagulation test data, blood gas test data, At least one of data from routine stool tests and tumor marker tests; The examination data includes at least one of DR image data, CT image data, MRI image data, PET image data, ultrasound image data, scale data, and physical examination data.

39. The life information processing system according to any one of claims 25 to 38, characterized in that: The patient status is a prediction result of the patient's future status and / or a judgment result of the patient's current status.

40. The life information processing system according to any one of claims 25 to 39, characterized in that: The analysis results include at least one of the following: Parameter values ​​extracted from the patient data, calculation results obtained based on the parameter values, changing trends of the patient data, out-of-limit alarms triggered by the patient data, the number of occurrences of the out-of-limit alarms, abnormal events represented by the patient data, the number of occurrences of the abnormal events, treatment events recorded in the patient data, and the number of occurrences of the treatment events.

41. The life information processing system according to any one of claims 25 to 40, characterized in that: The display of the cause prompt information indicating the cause of the disease includes: When the number of the causes of disease is at least two, the cause prompt information corresponding to each cause of disease is displayed in order of importance of the causes of disease.

42. The life information processing system according to any one of claims 25 to 41, characterized in that: The patient data also includes at least one of the following: background data, biochemical test data, and imaging examination data of the monitored object.

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