Prediction support system for high risk level of sepsis patient

Abstract

The invention relates to the technical field of disease risk assessment, in particular to a prediction support system for a high risk level of a sepsis patient. According to the abnormal degree of the physiological data in the abnormal time period of each category and the time interval between the abnormal time period and the adjacent abnormal time period, the importance degree of each category is obtained; selecting a feature category and a target time period based on the importance, and obtaining the severity of the target time period according to the importance difference between all categories in the target time period and the feature category and the number of categories with abnormal physiological data in the target time period; and acquiring a high risk value according to the change trend and the time interval of the severity of the target time period and the adjacent target time period, and adjusting and analyzing all types of physiological data in the time period by using the high risk value so as to predict the high risk of sepsis of the to-be-detected patient. According to the method, the difference of the physiological data in the time period in which the abnormal expression has the difference is dynamically expanded through the high-risk value, so that the accuracy of predicting the high-risk condition of sepsis is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of disease risk assessment, and in particular to a prediction support system for high-risk levels of sepsis patients. Background Art

[0002] Sepsis may develop from a relatively stable state to high-risk conditions such as septic shock in a short period of time. Early identification of high-risk patients with sepsis and intervention are the key to treating sepsis patients.

[0003] Existing methods usually use random forest models to predict the risk of high-risk situations in patients with sepsis. Sepsis is a highly heterogeneous disease. Due to the different order of symptom onset in different patients, there are differences in the abnormal manifestations of physiological data at different time periods. However, some physiological data are relatively close when the patient becomes ill and improves, so the physiological data of the time periods with different abnormal manifestations may be relatively close, which makes it difficult for the random forest model to effectively distinguish between high-risk and non-high-risk situations of sepsis, reducing the accuracy of predicting high-risk situations of sepsis. Summary of the invention

[0004] In order to solve the technical problem that the prediction accuracy of high-risk sepsis is low due to the close physiological data of the time periods with different abnormal manifestations, the purpose of the present invention is to provide a prediction support system for the high-risk level of sepsis patients, and the technical solution adopted is as follows: The present invention proposes a prediction support system for high-risk level of sepsis patients, the system comprising: A data acquisition module is used to collect several categories of physiological data of the patient to be tested at each moment in the analysis time period; The importance analysis module is used to obtain the abnormal time period of each category; according to the abnormal degree of physiological data in the abnormal time period of each category, and the time interval between the abnormal time period and its adjacent abnormal time periods, the importance of physiological data of each category is obtained; A severity analysis module, used to select a feature category based on the importance, record the abnormal period of the feature category as a target period; obtain the severity of each target period according to the difference between the importance of all categories and the feature category in each target period, and the number of categories of abnormal physiological data in each target period; The sepsis high-risk prediction module is used to obtain the high-risk value of each target period according to the change trend and time interval of the severity of each target period and its adjacent target period; and predict the high-risk risk of sepsis of the patient to be tested according to all categories of physiological data in the analysis time period adjusted by the high-risk value.

[0005] Further, obtaining the abnormal time period of each category includes: Normalize the absolute value of the difference between the physiological data of each category at each moment and the normal physiological preset value to obtain the abnormality of the physiological data of each category at each moment; The abnormal moment of each category is selected based on the abnormality degree; and the time period consisting of the continuous abnormal moments of each category is used as the abnormal time period of each category.

[0006] Furthermore, obtaining the importance of each category of physiological data includes: For each abnormal period of each category, the average value of the abnormal degree of the physiological data of each category at all times within the abnormal period is taken as the abnormal comprehensive value of the abnormal period; the sum of the time intervals between the abnormal period and all its first adjacent periods is taken as the first period interval value of the abnormal period; the cumulative sum of the ratios of the abnormal comprehensive value of all abnormal periods of each category to the first period interval value is calculated as the local importance value of the physiological data of each category; The proportion of the abnormal moments of each category in the analysis time period is used as the abnormal occupancy value of each category; The importance of each category of physiological data is obtained according to the local importance value and the abnormal occupancy value, and both the local importance value and the abnormal occupancy value are positively correlated with the importance.

[0007] Further, obtaining the severity of each target period includes: Obtaining the abnormal category in each target time period, wherein the abnormal category exists and the abnormal moment is in each target time period; obtaining the correlation between the physiological data of each abnormal category and the characteristic category in each target time period according to the difference in importance between each abnormal category and the characteristic category in each target time period, and the number of abnormal moments of each abnormal category in each target time period; Selecting a reference category from the abnormal categories in each target time period based on the correlation; The severity of each target period is obtained according to the correlation between the reference category and the feature category in each target period, the importance of the reference category, and the number of reference categories in each target period.

[0008] Furthermore, obtaining the correlation between the physiological data of each abnormal category and the characteristic category in each target time period includes: The proportion of abnormal moments of each abnormal category in each target time period in the corresponding target time period is counted; the absolute value of the difference between the importance of each abnormal category and the feature category in each target time period is negatively correlated and mapped, and the product of the mapping result and the proportion is used as the correlation between the physiological data of each abnormal category and the feature category in each target time period.

[0009] Further, the obtaining of the severity of each target period according to the correlation between the reference category and the feature category in each target period, the importance of the reference category, and the number of reference categories in each target period includes: The product of the correlation between each reference category and the feature category and the importance of the reference category in each target period is used as the reference severity value of each reference category in each target period; the cumulative sum of the reference severity values ​​of all reference categories in each target period is used as the local severity value of each target period; The severity of each target period is obtained according to the total number of abnormal categories and the local severity value in each target period; the total number of abnormal categories and the local severity value in each target period are both positively correlated with the severity.

[0010] Furthermore, obtaining the high-risk value for each target period includes: Performing a straight line fitting on the severity of each target period and its second adjacent period, normalizing the slope of the fitted straight line, and obtaining a severity trend value for each target period; taking the sum of the time intervals between each target period and all its second adjacent periods as the second period interval value for each target period; According to the severity of each target time period, the severe trend value and the second time period interval value, the high risk value of each target time period is obtained; the severity and the severe trend value are both positively correlated with the high risk value, and the second time period interval value is negatively correlated with the high risk value.

[0011] Furthermore, predicting the high risk of sepsis of the patient to be tested based on all categories of physiological data in the analysis time period adjusted by the high risk value includes: The sum of the high-risk value and the constant 1 is used as the adjustment coefficient for each target period; based on the adjustment coefficient, all categories of physiological data in the target period are weighted to obtain adjusted physiological data of each category at each moment in the target period; All categories of adjusted physiological data of the patient to be tested during the analysis period are input into the trained random forest model to predict the high risk of sepsis in the patient to be tested.

[0012] Furthermore, obtaining the high-risk value for each target period includes: Performing a straight line fitting on the severity of each target period and its second adjacent period, normalizing the slope of the fitted straight line, and obtaining a severity trend value for each target period; taking the sum of the time intervals between each target period and all its second adjacent periods as the second period interval value for each target period; According to the severity of each target time period, the severe trend value and the second time period interval value, the high risk value of each target time period is obtained; the severity and the severe trend value are both positively correlated with the high risk value, and the second time period interval value is negatively correlated with the high risk value.

[0013] Furthermore, the importance of the feature category is a maximum value among the importances of all categories.

[0014] Furthermore, the straight line fitting method is the least squares method.

[0015] The present invention has the following beneficial effects: In the embodiment of the present invention, the continuous abnormal changes of physiological data of each category and the abnormal degree of physiological data in the abnormal period are analyzed during the onset of sepsis through the time interval between each category of abnormal period and its adjacent abnormal period and the abnormal degree of physiological data in the abnormal period, and the importance of abnormal changes of sepsis signs is obtained; sepsis has many complications, and in order to accurately analyze the high-risk state of sepsis, feature categories and target periods are selected, and the difference in importance between each category and feature category in the target period presents each category reflecting abnormal changes of signs caused by sepsis, and combined with the number of categories of abnormal physiological data in the target period, the sepsis at the target time is analyzed. The severity of the target period is obtained by combining the severity of the target period and its adjacent target period to show the severity of the patient's sepsis during the target period. Combined with the time interval between the target period showing continuous abnormal conditions and its adjacent target period, the possibility of the patient being at high risk of sepsis during the target period is analyzed to obtain the high-risk value. The high-risk value is used to dynamically expand the difference in physiological data of periods with different abnormal manifestations, emphasize the physiological data of periods with greater abnormalities, and improve the recognition between physiological data of different abnormal manifestations, thereby improving the accuracy of prediction of high-risk sepsis. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A system structure diagram of a prediction support system for high-risk level of sepsis patients provided by one embodiment of the present invention; Figure 2 A structural diagram of a severity analysis module provided by an embodiment of the present invention; Figure 3 A schematic diagram of a computer device of a prediction support device for high-risk level of sepsis patients provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a prediction support system for high-risk level of sepsis patients proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] The specific scheme of the prediction support system for the high-risk level of sepsis patients provided by the present invention is described in detail below with reference to the accompanying drawings.

[0021] Embodiment 1: See also Figure 1 , which shows a system block diagram of a prediction support system for high-risk level of sepsis patients provided by an embodiment of the present invention, the system includes: a data acquisition module 110, an importance analysis module 120, a severity analysis module 130, and a sepsis high-risk prediction module 140.

[0022] The data collection module 110 is used to collect several categories of physiological data of the patient to be tested at each moment in the analysis time period.

[0023] Sepsis presents symptoms such as high fever or low temperature, accelerated heart rate, rapid breathing, and decreased blood pressure. The categories of physiological data in this embodiment include: body temperature, heart rate, blood pressure and respiratory rate. The temperature is measured by a temperature sensor, the heart rate is measured by an electrocardiogram monitor, the blood pressure is measured by a sphygmomanometer, and the respiratory rate sensor is used to measure the respiratory rate, and the physiological data of each category of the patient to be tested at each moment in the analysis time period is obtained. Among them, the patient to be tested suffers from sepsis.

[0024] In one implementation of the embodiment of the present invention, the data collection frequency of the temperature sensor, electrocardiogram monitor, blood pressure monitor and respiratory rate sensor is the same. The data collection frequency of the above instruments in the embodiment of the present invention is once every 5 seconds, and the duration of the analysis time period is an empirical value of 1 hour. The implementer can set it according to the specific situation.

[0025] The importance analysis module 120 is used to obtain the abnormal time period of each category; and obtain the importance of the physiological data of each category according to the abnormal degree of the physiological data in the abnormal time period of each category and the time interval between the abnormal time period and its adjacent abnormal time periods.

[0026] There are great differences in the magnitude, range and distribution of physiological data of different categories. In order to better analyze the abnormal changes of physiological data of each category, the time period of abnormal changes of physiological data of each category is screened to obtain the abnormal time period of the corresponding category.

[0027] Since different categories of physiological data show different performances during the onset of sepsis, it is necessary to analyze whether the changes in physiological data of each category can clearly reflect the changes in physical signs caused by sepsis. During the onset of sepsis, each category of physiological data will continuously show abnormal changes and the physiological data will deviate from the physiological data of healthy people. The importance of each category of physiological data is obtained based on the time interval between the abnormal period of each category and its adjacent abnormal period and the abnormal degree of physiological data in the abnormal period. The greater the importance, the more each category of physiological data can reflect the abnormal changes in physical signs caused by sepsis, and the more important the physiological data of this category is for analyzing the abnormal changes in physical signs caused by sepsis.

[0028] The severity analysis module 130 is used to select feature categories based on importance, record the abnormal period of the feature category as the target period, and obtain the severity of each target period based on the difference in importance between all categories and the feature category in each target period, and the number of categories of abnormal physiological data in each target period.

[0029] Sepsis has many complications. When sepsis is at high risk, different patients have different symptoms. For example, some patients have only one abnormal physiological data category at the beginning of the disease, and then multiple categories of physiological data become abnormal; some patients have multiple categories of physiological data at the beginning of the disease. Regardless of the onset, the earlier the physiological data with the most obvious abnormal changes in physical signs is observed, the more accurate the high-risk warning of sepsis patients can be.

[0030] The more important the category is, the more important it is for analyzing abnormal changes in physical signs caused by sepsis, and the changes in physiological data of this category can more quickly and accurately reflect the high-risk state of sepsis. Therefore, the category corresponding to the maximum importance of all categories of physiological data is taken as the feature category, and the abnormal period of the feature category is recorded as the target period.

[0031] Sepsis is characterized by the occurrence of multiple symptoms, that is, different symptoms gradually occur. The more complex the categories of abnormal physiological data, the more serious the impact of sepsis on the patient's health. Among them, the physiological data of the feature category can best reflect the abnormal changes in physical signs caused by sepsis. The difference in importance between each category and the feature category in the target time period shows that each category reflects the abnormal changes in physical signs caused by sepsis. If the category of abnormal physiological data in the target time period can obviously reflect the abnormal changes in physical signs caused by sepsis, the more serious the impact of sepsis on the patient's health in the target time period, the severity of the target time period is obtained.

[0032] The sepsis high-risk prediction module 140 is used to obtain a high-risk value for each target period according to the change trend and time interval of the severity between each target period and its adjacent target period; and predict the high-risk risk of sepsis for the patient to be tested according to all categories of physiological data in the analysis time period adjusted by the high-risk value.

[0033] The condition of sepsis changes dynamically and the body of sepsis patients is usually in a continuous abnormal state. The change trend of the severity of the target period and its adjacent target period shows the severity of the patient's sepsis during the target period. Combined with the time interval between the target period showing a continuous abnormal state and its adjacent target period, the possibility of the patient being at high risk of sepsis during the target period is analyzed to obtain the high-risk risk value.

[0034] The higher the high-risk value of sepsis is, the greater the risk of high-risk conditions occurring in the target period. The high-risk value is used to adjust all categories of physiological data in the target period, adaptively expand the differences in physiological data in time periods with different abnormal manifestations, enhance the generalization ability of the model, facilitate timely discovery of dynamic changes in the disease, and improve the accuracy of prediction of high-risk levels of sepsis.

[0035] Preferably, in some implementations of the embodiments of the present invention, the method for obtaining the abnormal time period is: normalizing the absolute value of the difference between the physiological data of each category at each moment and the normal physiological preset value to obtain the abnormality of the physiological data of each category at each moment; selecting the abnormal moment of each category based on the abnormality; and taking the time period consisting of the continuous abnormal moments of each category as the abnormal time period of each category. The normal physiological preset value of each category refers to the physiological data of healthy people. The greater the difference between the physiological data of each category at each moment and the normal physiological preset value, the greater the possibility that the physiological data deviates from the healthy population, and the greater the abnormality of the physiological data.

[0036] In a specific manner of the embodiment of the present invention, for the abnormality of each category at all moments within the analysis time period, the moment corresponding to the abnormality greater than a preset abnormality threshold is taken as the abnormal moment of each category.

[0037] In a specific embodiment of the present invention, the normal physiological preset values ​​of body temperature, heart rate, blood pressure and respiratory rate are taken as the empirical values ​​of 36.5 degrees Celsius, 80 times per minute, 114.5 mmHg and 16 times per minute, and the preset abnormal threshold is taken as the empirical value of 0.6, which can be set by the implementer according to the specific situation; the Norm function is used for normalization.

[0038] Preferably, in some possible implementation modes of the embodiments of the present invention, the method for obtaining the importance includes: for each abnormal time period of each category, taking the average of the abnormality of the physiological data of each category at all moments in the abnormal time period as the abnormal comprehensive value of the abnormal time period; taking the sum of the time intervals between the abnormal time period and all its first adjacent time periods as the first time period interval value of the abnormal time period; calculating the cumulative sum of the ratios of the abnormal comprehensive value of all abnormal time periods of each category to the first time period interval value as the local importance value of the physiological data of each category; taking the proportion of the abnormal moments of each category in the analysis time period as the abnormal occupancy value of each category; and obtaining the importance of the physiological data of each category based on the local importance value and the abnormal occupancy value.

[0039] The abnormal comprehensive value reflects the overall abnormality of the physiological data in each category during the abnormal period. The larger the abnormal comprehensive value, the more serious the abnormality of the physiological data in each category during the abnormal period, and the more it deviates from the physiological data of the healthy population; the smaller the interval value of the first period, the more continuous the abnormal period of each category. The physiological data of all abnormal periods of each category are integrated, and the importance of each category of physiological data to the analysis of abnormal changes in physical signs caused by sepsis is considered to obtain the local importance value of each category of physiological data. The larger the local importance value, the more important the physiological data of each category is to the analysis of abnormal changes in physical signs; the larger the abnormal occupancy value, the more physiological data with abnormal changes in each category, and the more serious the abnormality of each category of physiological data. Therefore, the local importance value and the abnormal occupancy value are positively correlated with the importance. The specific formula is expressed as: ; In the formula, H is the importance of each category of physiological data; N is the total number of abnormal moments in each category; Num is the total number of all moments in the analysis time period; is the abnormal occupancy value of each category; A is the total number of abnormal time periods of each category; is the comprehensive abnormal value of the a-th abnormal period of each category; is the first period interval value of the a-th abnormal period of each category; The local importance value of the physiological data for each category.

[0040] In a specific embodiment of the present invention, the adjacent previous and next abnormal time periods of each abnormal time period of each category are recorded as first adjacent time periods; the time interval between any two abnormal time periods of each category is equal to the length of time between the end time of the previous abnormal time period and the start time of the next abnormal time period.

[0041] In a specific manner of the embodiment of the present invention, the category corresponding to the maximum value among the importance levels of all categories of physiological data is used as the feature category.

[0042] See also Figure 2 , which shows a structural diagram of a severity analysis module provided by an embodiment of the present invention. The severity analysis module includes: a correlation analysis unit 131, a reference category screening unit 132, and a severity analysis unit 133.

[0043] Correlation analysis unit 131: used to obtain the abnormal category in each target time period, and the abnormal moment of the abnormal category is in each target time period; according to the difference in importance between each abnormal category and the feature category in each target time period, and the number of abnormal moments of each abnormal category in each target time period, the correlation of the physiological data of each abnormal category and the feature category in each target time period is obtained.

[0044] Due to the different incidence of sepsis patients, not all categories of physiological data in the target period can reflect abnormal changes in physical signs. It is necessary to screen the categories with abnormal physiological data in the target period to obtain abnormal categories. Physiological data of characteristic categories are the most important for analyzing abnormal changes in physical signs caused by sepsis; if the importance of abnormal categories and physiological data of characteristic categories in the target period is closer to that of physiological data of characteristic categories in the analysis of abnormal changes in physical signs caused by sepsis, and the abnormal duration of abnormal categories in the target period is closer to the duration of the target period, it reflects that the correlation between physiological data of abnormal categories and characteristic categories in the target period is greater, and the correlation is obtained.

[0045] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the correlation includes: counting the proportion of abnormal moments of each abnormal category in each target time period in the corresponding target time period; performing negative correlation mapping on the absolute value of the difference between the importance of each abnormal category and the feature category in each target time period, and taking the product of the mapping result and the proportion as the correlation between the physiological data of each abnormal category and the feature category in each target time period. In the embodiment of the present invention, the negative number of the absolute value of the difference between the importance of each abnormal category and the feature category in the target time period is first taken, and the negative number is used as the exponent of an exponential function with a natural constant as the base to achieve negative correlation mapping of the absolute value of the difference; in other embodiments, negative correlation mapping can also be achieved by taking negative numbers and function conversion.

[0046] If the proportion of abnormal moments of each abnormal category in the target period is larger, it means that the abnormal duration of each abnormal category in the target period is closer to the duration of the target period, and the correlation between each abnormal category and the feature category in the target period is higher. If the difference between the importance of each abnormal category and the feature category in each target period is smaller, the importance of the abnormal category is closer to the importance of the feature category, and the correlation between each abnormal category and the feature category in the target period is higher.

[0047] Reference category screening unit 132: selects reference categories from abnormal categories in each target time period based on the relevance.

[0048] In order to reduce the amount of calculation and improve the accuracy of sepsis high-risk analysis, a reference category with a high degree of correlation with the physiological data of the feature category in the target period is selected from the abnormal category. For the correlation between all abnormal categories and the feature category in each target period, the correlation greater than the preset correlation threshold is used as the reference category in each target period.

[0049] In a specific manner of an embodiment of the present invention, the preset relevant threshold is an empirical value of 0.7, and the implementer can set it according to the specific situation.

[0050] The severity analysis unit 133 is used to obtain the severity of each target period according to the correlation between the reference category and the feature category in each target period, the importance of the reference category, and the number of reference categories in each target period.

[0051] Preferably, in some possible implementation methods of the embodiments of the present invention, the method for obtaining the severity includes: taking the product of the correlation between each reference category and the feature category and the importance of the reference category in each target time period as the reference severity value of each reference category in each target time period; taking the cumulative sum of the reference severity values ​​of all reference categories in each target time period as the local severity value of each target time period; and obtaining the severity of each target time period based on the total number of abnormal categories and the local severity value in each target time period.

[0052] If the correlation between the reference category and the feature category in the target period is greater, and the importance of the reference category in the target period is greater, it means that the patient has abnormalities in many aspects of the body in the target period and the abnormal changes in the physiological data of the reference category in the target period are more obvious, then the sepsis has a more serious impact on the patient's health in the target period, and the local severity value is larger. If the total number of abnormal categories in the target period is greater, it means that the categories of physiological data with abnormal changes in the target period are richer, and the sepsis has a more serious impact on the patient's health in the target period. Therefore, the total number of abnormal categories and the local severity value in each target period are positively correlated with the severity.

[0053] In a specific implementation of the embodiment of the present invention, the severity of each target period is expressed by the formula: ; Where Z is the severity of each target period; M is the total number of abnormal categories in each target period; B is the total number of reference categories in each target period; is the correlation between the bth reference category and the feature category in each target period; is the importance of the bth reference category in each target period; is the reference severity value for each reference category under each target period; is the local severity value of each target period; Norm is the normalization function.

[0054] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the high-risk value includes: performing a straight line fitting on the severity of each target period and its second adjacent period, normalizing the slope of the fitted line, and obtaining the severe trend value of each target period; taking the sum of the time intervals between each target period and all its second adjacent periods as the second period interval value of each target period; and obtaining the high-risk value of each target period according to the severity, severe trend value and second period interval value of each target period. In a specific implementation of the embodiments of the present invention, a two-dimensional coordinate system is established with time as the horizontal axis and severity as the vertical axis, and the severity of each target period and its second adjacent period is mapped into the two-dimensional coordinate system to obtain the corresponding coordinate points, and then the least squares method is used to perform a straight line fitting to obtain a fitted straight line. It should be noted that the horizontal coordinate of the coordinate point of the target period is equal to the middle moment of the target period.

[0055] The greater the severity of the target period, the greater the possibility of high-risk sepsis in the patient; if the severity trend value is larger, the possibility of high-risk sepsis in the local range of the target period is gradually increasing, indicating that the condition of sepsis deteriorates sharply in the target period, and the possibility of high-risk sepsis in the target period is greater; if the second period interval value is smaller, the patient's body is in a continuous abnormal state in the local range of the target period, the patient is continuously affected by the inflammation caused by sepsis, and the possibility of high-risk sepsis in the target period is greater. Therefore, the severity and severity trend values ​​are positively correlated with the high-risk value, and the second period interval value is negatively correlated with the high-risk value.

[0056] The high-risk value F for each target period is expressed by the formula: ; Where Z is the severity of each target period; k is the slope of the fitted straight line obtained by linear fitting of the severity of each target period and its second adjacent period; is the severe trend value of each target period; T2 is the second period interval value of each target period; exp is an exponential function with a natural constant as the base; Norm is a normalization function.

[0057] In a specific embodiment of the present invention, the first three adjacent target periods and the last three adjacent target periods of each target period are recorded as the second adjacent periods. It should be noted that in this embodiment, the number of first adjacent periods of the abnormal period is less than the number of second adjacent periods of the target period, and the high-risk values ​​of the first three and last three target periods in the analysis time period are directly set to a constant of 1.

[0058] In one implementation of an embodiment of the present invention, a method for predicting a high risk of sepsis is as follows: taking the sum of a high risk value and a constant 1 as an adjustment coefficient for each target period; weighting all categories of physiological data within the target period based on the adjustment coefficient to obtain adjusted physiological data of each category at each moment within the target period; inputting all categories of adjusted physiological data of the patient to be tested within the analysis time period into a trained random forest model to predict the high risk of sepsis for the patient to be tested.

[0059] The training process of the random forest model is: obtain physiological data of several categories of two types of training patients within a certain period of time, label the training patients at high risk of sepsis as high risk, and label the training patients at non-high risk of sepsis as non-high risk, input the above physiological data into the random forest model, and train the random forest model. The adjusted physiological data of all categories of the tested patients in the analysis time period are input into the trained random forest model, and the output of the model is the predicted probability of each label. When the predicted probability of the high-risk label is, the greater the possibility that the patient to be tested is at a high risk level of sepsis. Among them, the training process of the random forest model is well known to those skilled in the art and will not be repeated here.

[0060] It should be noted that all categories of adjusted physiological data within the analysis time period include: all categories of adjusted physiological data within the target time period, and all categories of physiological data at other times within the analysis time period except the target time period.

[0061] So far, the present invention is completed.

[0062] Embodiment 2: Figure 3 A computer device schematic diagram of a prediction support device for a high-risk level of a sepsis patient provided by an embodiment of the present invention. For example, Figure 3As shown, the computer device includes: a memory 201, a processor 202, and a computer program 203 stored in the memory 201 and running on the processor 202, wherein when the processor 202 executes the computer program 203, the computer device can execute any one of the prediction support systems for high-risk levels of sepsis patients introduced above.

[0063] In addition, an embodiment of the present application also protects a device, which may include a memory and a processor, wherein the memory stores an executable program code, and the processor is used to call and execute the executable program code to execute a prediction support system for high-risk levels of sepsis patients provided in an embodiment of the present application.

[0064] In this embodiment, the functional modules of the device can be divided according to the above method example. For example, each functional module can be corresponded, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0065] It should be understood that the device provided in this embodiment is used to execute the above-mentioned prediction support system for the high-risk level of sepsis patients, and thus can achieve the same effect as the above-mentioned implementation method.

[0066] In the case of an integrated unit, the device may include a processing module and a storage module. When the device is applied to a device, the processing module may be used to control and manage the actions of the device. The storage module may be used to support the device to execute mutual program codes, etc.

[0067] The processing module may be a processor or a controller, which may implement or execute various exemplary logic blocks, modules and circuits disclosed in the present application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module may be a memory.

[0068] Embodiment 3: This embodiment also provides a computer-readable storage medium, in which a computer program code is stored. When the computer program code is executed on a computer, the computer executes the above-mentioned related method steps to implement a prediction support system for a high-risk level of a sepsis patient provided in the above embodiment.

[0069] Among them, the device and computer-readable storage medium provided in this embodiment are used to execute the corresponding system provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding system provided above, and will not be repeated here.

[0070] It should be noted that the sequence of the above embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0072] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A prediction support system for high-risk level of sepsis patients, characterized in that: The system includes: A data acquisition module is used to collect several categories of physiological data of the patient to be tested at each moment in the analysis time period; The importance analysis module is used to obtain the abnormal time period of each category; according to the abnormal degree of physiological data in the abnormal time period of each category, and the time interval between the abnormal time period and its adjacent abnormal time periods, the importance of physiological data of each category is obtained; A severity analysis module, used to select a feature category based on the importance, record the abnormal period of the feature category as a target period; obtain the severity of each target period according to the difference between the importance of all categories and the feature category in each target period, and the number of categories of abnormal physiological data in each target period; The sepsis high-risk prediction module is used to obtain the high-risk value of each target period according to the change trend and time interval of the severity of each target period and its adjacent target period; and predict the high-risk risk of sepsis of the patient to be tested according to all categories of physiological data in the analysis time period adjusted by the high-risk value.

2. A prediction support system for high-risk level of sepsis patients according to claim 1, characterized in that: The obtaining of abnormal time periods of each category includes: Normalize the absolute value of the difference between the physiological data of each category at each moment and the normal physiological preset value to obtain the abnormality of the physiological data of each category at each moment; The abnormal moment of each category is selected based on the abnormality degree; and the time period consisting of the continuous abnormal moments of each category is used as the abnormal time period of each category.

3. A prediction support system for high-risk level of sepsis patients according to claim 2, characterized in that: The obtaining of the importance of each category of physiological data includes: For each abnormal period of each category, the average value of the abnormal degree of the physiological data of each category at all times within the abnormal period is taken as the abnormal comprehensive value of the abnormal period; the sum of the time intervals between the abnormal period and all its first adjacent periods is taken as the first period interval value of the abnormal period; the cumulative sum of the ratios of the abnormal comprehensive value of all abnormal periods of each category to the first period interval value is calculated as the local importance value of the physiological data of each category; The proportion of the abnormal moments of each category in the analysis time period is used as the abnormal occupancy value of each category; The importance of each category of physiological data is obtained according to the local importance value and the abnormal occupancy value, and both the local importance value and the abnormal occupancy value are positively correlated with the importance.

4. A prediction support system for high-risk level of sepsis patients according to claim 2, characterized in that: The obtaining of the severity of each target period includes: Obtaining the abnormal category in each target time period, wherein the abnormal category exists and the abnormal moment is in each target time period; obtaining the correlation between the physiological data of each abnormal category and the characteristic category in each target time period according to the difference in importance between each abnormal category and the characteristic category in each target time period, and the number of abnormal moments of each abnormal category in each target time period; Selecting a reference category from the abnormal categories in each target time period based on the correlation; The severity of each target period is obtained according to the correlation between the reference category and the feature category in each target period, the importance of the reference category, and the number of reference categories in each target period.

5. A prediction support system for high-risk level of sepsis patients according to claim 4, characterized in that: The step of obtaining the correlation between the physiological data of each abnormal category and the characteristic category in each target time period includes: The proportion of abnormal moments of each abnormal category in each target time period in the corresponding target time period is counted; the absolute value of the difference between the importance of each abnormal category and the feature category in each target time period is negatively correlated and mapped, and the product of the mapping result and the proportion is used as the correlation between the physiological data of each abnormal category and the feature category in each target time period.

6. A prediction support system for high-risk level of sepsis patients according to claim 4, characterized in that: The obtaining of the severity of each target period according to the correlation between the reference category and the feature category in each target period, the importance of the reference category, and the number of reference categories in each target period includes: The product of the correlation between each reference category and the feature category and the importance of the reference category in each target period is used as the reference severity value of each reference category in each target period; the cumulative sum of the reference severity values ​​of all reference categories in each target period is used as the local severity value of each target period; The severity of each target period is obtained according to the total number of abnormal categories and the local severity value in each target period; the total number of abnormal categories and the local severity value in each target period are both positively correlated with the severity.

7. A prediction support system for high-risk level of sepsis patients according to claim 1, characterized in that: The step of obtaining the high-risk value for each target period includes: Performing a straight line fitting on the severity of each target period and its second adjacent period, normalizing the slope of the fitted straight line, and obtaining a severity trend value for each target period; taking the sum of the time intervals between each target period and all its second adjacent periods as the second period interval value for each target period; According to the severity of each target time period, the severe trend value and the second time period interval value, the high risk value of each target time period is obtained; the severity and the severe trend value are both positively correlated with the high risk value, and the second time period interval value is negatively correlated with the high risk value.

8. A prediction support system for high-risk level of sepsis patients according to claim 1, characterized in that: The method of predicting the high risk of sepsis of the patient to be tested based on all categories of physiological data in the analysis time period adjusted by the high risk value includes: The sum of the high-risk value and the constant 1 is used as the adjustment coefficient for each target period; based on the adjustment coefficient, all categories of physiological data in the target period are weighted to obtain adjusted physiological data of each category at each moment in the target period; All categories of adjusted physiological data of the patient to be tested during the analysis period are input into the trained random forest model to predict the high risk of sepsis in the patient to be tested.

9. A prediction support system for high-risk level of sepsis patients according to claim 1, characterized in that: The importance of the feature category is the maximum value among the importances of all categories.

10. A prediction support system for high-risk level of sepsis patients according to claim 7, characterized in that: The method of straight line fitting is the least square method.

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