Prediction method and system for sepsis deterioration based on traditional Chinese medicine big data

Abstract

The invention relates to the technical field of deterioration prediction, in particular to a sepsis deterioration prediction method and system based on traditional Chinese medicine big data, and the method comprises the following steps: based on a sepsis patient, extracting the traditional Chinese medicine constitution and physiological index data of the patient, comparing the data with normal constitution data and standard physiological indexes provided by the traditional Chinese medicine big data, and according to the data deviation, determining the sepsis deterioration of the patient. And evaluating the physique state of the patient to obtain a patient physique evaluation result. According to the method, the physical state and the disease development risk of the patient can be accurately evaluated by combining the analysis of the traditional Chinese medicine physique and the physical indexes and utilizing the comprehensive comparative analysis of the physical data such as the pulse condition and the tongue condition of the traditional Chinese medicine and the traditional physical indexes, and the high-risk patient can be identified by analyzing the fluctuation of the data and the change of the time sequence. The method effectively predicts the instability and deterioration probability of the illness state, carries out dynamic evaluation according to the physical condition of the patient and the severity of sepsis, provides data support for treatment, and enhances the accuracy and timeliness of medical prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of deterioration prediction, and in particular to a method and system for predicting sepsis deterioration based on traditional Chinese medicine big data. Background Art

[0002] The field of deterioration prediction technology focuses on predicting disease trends and changes in patients' conditions. The technology uses information extracted from preclinical and real-time data, such as physiological parameters, laboratory test results, and patients' historical medical records to assess the probability of disease deterioration. The main applications of this type of technology include improving the efficiency of hospital treatment, optimizing resource allocation, reducing emergencies and mortality, and providing medical providers with data-supported decision-making tools. Deterioration prediction models are often applied to high-risk and high-variability diseases, such as cardiovascular disease, chronic obstructive pulmonary disease, and infectious diseases, with the goal of improving patients' clinical outcomes through early warning systems.

[0003] Among them, the sepsis deterioration prediction method is a method used to predict the deterioration trend of the condition of sepsis patients. The method analyzes the patient's clinical data, including vital signs, blood test results, immune response and other information, to identify early signs that lead to worsening of the condition. The main purpose of the prediction method is to promptly identify high-risk patients in the early stages of sepsis, so as to intervene in advance, optimize treatment plans, reduce complications and improve survival rates. By accurately predicting the patient's condition, the medical team can manage resources and adjust treatment strategies more effectively, significantly improving the individualization and accuracy of treatment.

[0004] Traditional prediction methods mainly rely on modern medical parameters, such as physiological indicators and laboratory results, and rarely take into account the overall constitution and individual differences of patients, which limits the comprehensiveness and personalization of predictions. Especially when dealing with highly variable and complex diseases such as sepsis, analysis based on a single data source alone cannot fully predict the trend of disease changes in all patients. For example, for patients whose physiological parameters have not yet shown obvious abnormalities, but who are actually weak and at high risk of worsening, existing technologies may not be able to provide effective warnings in a timely manner, resulting in untimely or inaccurate treatment interventions, which in turn affects the management of the disease and the treatment outcomes of patients. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for predicting sepsis deterioration based on big data of traditional Chinese medicine.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for predicting the deterioration of sepsis based on big data of traditional Chinese medicine, comprising the following steps: S1: Based on sepsis patients, extract the TCM constitution and physiological index data of the patients, compare them with the normal constitution data and standard physiological indexes provided by TCM big data, evaluate the patient's physical condition according to the data deviation, and obtain the patient's physical condition evaluation results; S2: Based on the patient's physical condition assessment result, by analyzing the fluctuations of the patient's TCM physical condition and physiological condition indicator data, assessing the instability of the patient's condition, and analyzing the changes in the patient's physical condition and the instability of the condition over a period of time, assessing the changing trend of the patient's physical condition, and obtaining the patient's physical condition change information; S3: Based on the patient's sepsis condition detection data, the severity of the patient's sepsis condition is evaluated according to the patient's infection type, number of types and duration, and a severity assessment result is obtained; S4: Based on the patient's physical condition assessment results, the patient's physical condition change information and the severity assessment results, the treatment records of similar sepsis patients are extracted, and according to the status data deviation, the probability of sepsis deterioration under differentiated treatment measures is analyzed, and the trend of sepsis deterioration is predicted to obtain sepsis deterioration prediction information.

[0007] The present invention is improved in that the steps of obtaining the patient's physical fitness assessment result are: S111: Based on the sepsis patient, collect the patient's TCM constitution and physiological index data, the TCM constitution data includes the patient's pulse and tongue condition, the physiological index data includes the user's body temperature and blood index, and obtain the patient's real-time status information; S112: extracting standard values ​​of TCM constitution data and standard physiological index values ​​based on the patient's real-time status information to obtain constitution-related data; S113: Based on the constitution-related data, the formula: ; Calculate the patient's physical fitness score and obtain the patient's physical fitness assessment result; in, Score the patient's physical condition. and is the influence coefficient, For the The actual measured value of each TCM constitution data point, For the The standard value of each TCM constitution data point, For the The actual measured value of each physiological indicator data point, For the The standard physiological index value of each physiological index data point, For the Adjustment parameters, For the Adjustment parameters, For the The weight coefficients, For the The weight coefficients, is the total number of data points of TCM constitution, is the total number of data points of physiological indicators, is the base of natural logarithms.

[0008] The present invention is improved in that the step of assessing the instability of the patient's condition is: S211: Based on the patient's physical fitness assessment result, extract the patient's TCM physical fitness and physiological condition index data within a period of time, and calculate the average value of the data to obtain unstable correlation data; S212: Based on the unstable association data, by formula: ; Calculate the patient's instability index; in, is the patient's instability index, and is the weight factor, For the The actual measured value of each TCM constitution data point, For the The actual measured value of each physiological indicator data point, is the average value of TCM constitution data. is the average value of physiological index data, is the total number of data points of physiological indicators, is the base of natural logarithms; S213: Based on the instability index of the patient, according to the numerical value of the instability index of the patient, the instability of the patient's condition is evaluated to obtain an instability evaluation result.

[0009] The present invention is improved in that the step of obtaining the patient's physical condition change information is: S221: Based on the patient's physical fitness assessment result and instability assessment result, extract the patient's physical fitness score and instability index within a period of time, and record the corresponding time points to obtain physical fitness trend correlation data; S222: Based on the physical condition trend correlation data, the formula: ; Calculate the patient's physical trend index to obtain the patient's physical change information; in, is the patient's physical tendency index, and is the weight factor, Indicates time point The physical fitness score, Indicates time point The instability index, is the time interval.

[0010] The present invention is improved in that the steps of obtaining the severity assessment result are: S311: extracting the infection type, number of types and duration data of the patient based on the patient's sepsis status detection data to obtain severity-related data; S312: Based on the severity association data, by formula: ; Calculate the patient's severity score; in, Score the severity of the patient's is the number of infection types, for The duration of infection, For the The severity weight of each infection type; S313: Based on the severity score of the patient, the severity of the patient's sepsis condition is assessed according to the severity score to obtain a severity assessment result.

[0011] The present invention is improved in that the step of obtaining the probability of worsening of sepsis is: S411: based on the patient's physical fitness assessment result, the patient's physical fitness change information and the severity assessment result, extracting treatment records of similar sepsis patients, analyzing the average deterioration probability of similar sepsis patients under differentiated treatment measures, and extracting the average physical fitness trend index, average physical fitness score and average severity score of similar patients to obtain deterioration probability association data; S412: Based on the deterioration probability association data, by formula: ; Calculate the probability of worsening of sepsis under target treatment measures and obtain the worsening probability assessment result; in, , and is the weight factor, is the average probability of deterioration of similar sepsis patients under target treatment measures, and are the current patient's physical fitness score and the average physical fitness score of the same group, and They are the current patient's physical fitness trend index and the average physical fitness trend index of the same group. and are the severity score of the current patient and the average severity score of the same group, The probability of worsening of sepsis.

[0012] The present invention is improved in that the step of obtaining the sepsis deterioration prediction information is: S421: Based on the patient's physical fitness assessment result, the patient's physical fitness change information and the severity assessment result, and according to the treatment records of similar sepsis patients, extract the average physical fitness score, physical fitness trend index, severity score and average deterioration rate of similar sepsis patients to obtain deterioration trend associated data; S422: Based on the deterioration trend correlation data, by formula: ; Calculate the patient's sepsis exacerbation trend index; in, is the patient's sepsis worsening trend index, is the average deterioration rate of similar sepsis patients, and are the current patient's physical fitness score and the average physical fitness score of the same group, and They are the current patient's physical fitness trend index and the average physical fitness trend index of the same group. and are the severity score of the current patient and the average severity score of the same group, , and is the weight factor; S423: Based on the patient's sepsis deterioration trend index, the deterioration probability assessment result is integrated to obtain sepsis deterioration prediction information.

[0013] A sepsis exacerbation prediction system based on TCM big data, the sepsis exacerbation prediction system based on TCM big data is used to execute the above-mentioned sepsis exacerbation prediction method based on TCM big data, the system comprising: The patient constitution analysis module extracts the TCM constitution and physiological index data of patients with sepsis, compares them with the normal constitution data and standard physiological indexes provided by TCM big data, and evaluates the patient's constitution status according to the data deviation to obtain the patient's constitution evaluation result; The instability analysis module evaluates the instability of the patient's condition based on the patient's physical condition evaluation result by analyzing the fluctuation of the patient's TCM physical condition and physiological condition indicator data to obtain an instability evaluation result; The physical condition trend analysis module analyzes the physical condition changes and the instability changes of the patient over a period of time based on the physical condition assessment results and the instability assessment results of the patient, assesses the changing trend of the patient's physical condition, and obtains the patient's physical condition change information; The severity analysis module evaluates the severity of the patient's sepsis condition based on the patient's sepsis condition detection data and according to the patient's infection type, number of types and duration, and obtains a severity assessment result; The deterioration prediction module extracts the treatment records of similar sepsis patients based on the patient's physical fitness assessment results, the patient's physical fitness change information and the severity assessment results, analyzes the probability of sepsis deterioration under differentiated treatment measures for users according to the status data deviation, and predicts the trend of sepsis deterioration to obtain sepsis deterioration prediction information.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by combining the analysis of TCM constitution and physiological indicators, and utilizing comprehensive comparative analysis of TCM constitution data such as pulse and tongue with traditional physiological indicators, it is possible to accurately assess the patient's physical condition and analyze the changing trend of the physical condition. Combined with the sepsis severity analysis, the risk of sepsis development is assessed, and by analyzing the fluctuations of data and changes in time series, the probability of sepsis deterioration is effectively predicted. Dynamic assessment is performed based on the patient's physical condition and the deterioration trend of sepsis, providing data support for treatment and enhancing the accuracy and timeliness of medical predictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of the method of the present invention; Figure 2 A flow chart for obtaining the patient's physical fitness assessment results for the present invention; Figure 3 A flow chart for assessing the instability of a patient's condition for the present invention; Figure 4 A flow chart of the present invention for obtaining information on changes in the patient's physical condition; Figure 5 A flow chart for obtaining severity assessment results for the present invention; Figure 6 A flow chart for obtaining the probability of worsening of sepsis for the present invention; Figure 7 The present invention is a flow chart for obtaining sepsis exacerbation prediction information. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0018] See also Figure 1 The present invention provides a technical solution: a method for predicting the deterioration of sepsis based on big data of traditional Chinese medicine, comprising the following steps: S1: Based on sepsis patients, extract the TCM constitution and physiological index data of the patients. The TCM constitution data includes the pulse and tongue of the patients, and the physiological index data includes the user's body temperature and blood indexes. Compare them with the normal constitution data and standard physiological indexes provided by TCM big data. According to the data deviation, evaluate the patient's physical condition and obtain the patient's physical evaluation results; S2: Based on the results of the patient's physical fitness assessment, by analyzing the fluctuations of the patient's TCM physical fitness and physiological condition index data, assess the instability of the patient's condition, and analyze the changes in the patient's physical fitness state and the instability of the condition over a period of time, assess the changing trend of the patient's physical fitness, and obtain information on the patient's physical fitness changes; S3: Based on the patient's sepsis condition detection data, the severity of the patient's sepsis condition is evaluated according to the patient's infection type, number of types and duration, and a severity assessment result is obtained; S4: Based on the patient's physical condition assessment results, the patient's physical condition change information and the severity assessment results, the treatment records of similar sepsis patients are extracted. According to the status data deviation, the probability of sepsis deterioration under differentiated treatment measures is analyzed, and the trend of sepsis deterioration is predicted to obtain sepsis deterioration prediction information.

[0019] The patient's physical condition assessment results include the body mass index deviation value and physical condition type classification. The patient's physical condition change information includes the physical instability index, change rate and trend prediction value. The severity assessment results include the infection intensity score, inflammatory response level, duration information and severity level. The sepsis deterioration prediction information includes deterioration probability information, deterioration trend prediction information and treatment plan sensitivity information.

[0020] See also Figure 2 , the steps to obtain the patient's physical fitness assessment results are: S111: Based on the sepsis patient, collect the patient's TCM constitution and physiological index data, the TCM constitution data includes the patient's pulse and tongue condition, the physiological index data includes the user's body temperature and blood index, and obtain the patient's real-time status information; During the collection phase of TCM constitution and physiological index data of sepsis patients, staff need to record the patient's pulse and tongue in detail, and measure body temperature and blood indicators such as red blood cell count and white blood cell count. The data are obtained in a medical environment by using a digital thermometer and an automatic blood analyzer. Accurate recording of data is key to ensure data quality and reliability of results in subsequent steps. The collected data will be entered through the hospital's information system and compared with historical data stored in the database, so that the patient's status can be monitored in real time, providing basic data for subsequent analysis and evaluation.

[0021] S112: extracting standard values ​​of TCM constitution data and standard physiological index values ​​based on the patient's real-time status information to obtain constitution-related data; After obtaining the patient's real-time status information, it is necessary to extract the standard values ​​of the TCM constitution and physiological indicators corresponding to the patient from the database. The standard value is an average value obtained by statistical methods based on a large number of previous patient data. For example, the standard value of pulse may be based on the average of pulse speed and pulse pressure, while the standard value of tongue may describe the common characteristics of tongue quality and tongue coating. This step requires the database management system to support efficient data retrieval and matching functions to ensure that the patient's current data can be quickly and accurately compared with the standard value. The obtained constitution-related data will provide a reference for evaluating the patient's physical condition.

[0022] S113: Based on the constitution-related data, the formula is: ; Calculate the patient's physical fitness score and obtain the patient's physical fitness assessment result; in, Score the patient's physical condition. and is the influence coefficient, For the The actual measured value of each TCM constitution data point, For the The standard value of each TCM constitution data point, For the The actual measured value of each physiological indicator data point, For the The standard physiological index value of each physiological index data point, For the Adjustment parameters, For the Adjustment parameters, For the The weight coefficients, For the The weight coefficients, is the total number of data points of TCM constitution, is the total number of data points of physiological indicators, is the base of natural logarithms.

[0023] formula: ; Parameter details and how to obtain them: and :The parameter is the influence coefficient, which is used to adjust the contribution weight of TCM constitution data and physiological index data to the overall score. Usually, the coefficient is derived based on historical data analysis or expert experience, reflecting the importance of different data sets to health assessment. For example, if research shows that TCM constitution data is more sensitive to predicting a patient's health status, Probably relatively high.

[0024] and : It is The actual measured value of each TCM constitution data point, such as pulse strength or frequency, It is the corresponding standard value. The value is usually obtained through professional Chinese medicine diagnostic equipment and compared with the established health standard.

[0025] and : It is The actual measured value of a physiological indicator data point, such as blood pressure or blood sugar, It is the standard health value of this indicator. The data is obtained through medical testing equipment and evaluated according to medical standards.

[0026] and : Adjustment parameters are used to control the impact strength of deviations, which are set according to the sensitivity and volatility of different indicators and determined based on statistical data to ensure appropriate response to outliers.

[0027] and : The weight coefficient defines the importance of each indicator in the total score. The weight is determined through statistical analysis and reflects the actual effectiveness of each indicator in predicting the patient's health status.

[0028] Calculation example: Set the following parameters: TCM constitution data points , standard value , 1. Physiological index data points , standard value , 1. Influence coefficient , , adjust the parameters , , weight coefficient , .

[0029] For TCM constitution score calculation: ; ; For physiological index score calculation: ; ; Physical fitness score: ; Calculation results The patient's health status is quantified, and a lower score may indicate that the patient's health status deviates significantly from the standard health status and requires further medical intervention or diagnosis.

[0030] See also Figure 3 , the steps to assess the instability of the patient's condition are: S211: Based on the patient's physical fitness assessment results, extract the patient's TCM physical fitness and physiological condition index data over a period of time, and calculate the average value of the data to obtain unstable correlation data; After the patient's TCM constitution and physiological indicator data are collected, the average value of the data is extracted. The process involves calculating the arithmetic mean of the data points. TCM constitution data such as pulse and tongue are evaluated by professional TCM practitioners through observation and feeling, and physiological indicators such as heart rate and body temperature are directly measured by medical instruments. The data are usually stored in medical health records. The average physical index and average physiological index of each patient are automatically calculated by the program. The obtained data are used for health status analysis. Data integration provides a preliminary understanding of fluctuations in the patient's health status, laying the foundation for more detailed health monitoring and management.

[0031] S212: Based on unstable correlation data, through the formula: ; Calculate the patient's instability index; in, is the patient's instability index, and is the weight factor, For the The actual measured value of each TCM constitution data point, For the The actual measured value of each physiological indicator data point, is the average value of TCM constitution data. is the average value of physiological index data, is the total number of data points of physiological indicators, is the base of natural logarithms; formula: ; Parameter details and how to obtain them: and :Weight factors provide weighting when evaluating the instability of TCM constitution and physiological index data. The weights are quantitatively determined based on previous data analysis or medical research to ensure that the contribution of each data type in the total instability index is appropriately reflected. These values ​​are generally determined by statistically analyzing the volatility of patient data.

[0032] : No. The actual measured values ​​of each TCM constitution data point, including pulse condition, tongue condition, etc. These data are usually measured by TCM professional equipment.

[0033] : No. The actual measured values ​​of physiological indicator data points, such as blood pressure, blood sugar, etc., are directly measured by medical monitoring equipment.

[0034] and : The average value is obtained by and It is calculated by arithmetic mean and reflects the average physical or physiological state over a certain period of time.

[0035] Calculation example: Set the following parameters: TCM Constitution Dataset , physiological index data set , weight factor , .

[0036] Calculate the average: ; ; Calculate the standard deviation: ; ; Calculate the instability index: ; Calculation results , indicating the degree of fluctuation in the patient's overall health status during the observation period. The higher this index is, the more unstable the patient's health status is and the more medical intervention or monitoring may be needed.

[0037] S213: Based on the instability index of the patient, according to the numerical value of the instability index of the patient, the instability of the patient's condition is evaluated to obtain an instability evaluation result.

[0038] After obtaining the patient's instability index, the doctor will assess the instability of the patient's condition based on the size of this value. The assessment process includes dividing the instability index into thresholds. For example, it can be set that if the instability index is less than 1, the patient's condition is considered to be relatively stable; if the index is greater than 1, the condition is considered to be unstable and requires closer observation or adjustment of treatment measures. This assessment method can help the medical team more effectively monitor and respond to changes in the patient's health status, thereby providing more personalized and timely medical interventions.

[0039] See also Figure 4 , the steps to obtain the patient's physical condition change information are: S221: Based on the patient's physical fitness assessment results and instability assessment results, extract the patient's physical fitness score and instability index within a period of time, and record the corresponding time points to obtain physical fitness trend correlation data; Based on the results of the physical fitness and instability assessment, the physical fitness score and instability index of the patient over a period of time are extracted from the historical records. The data points will be associated with their respective timestamps. To ensure the accuracy of the data, the medical team will use professional data recording tools to regularly update the patient's physical fitness score and instability index. In this way, the patient's health trend can be dynamically monitored. The time series data will be entered into a dedicated database that supports efficient time query and data analysis so that medical experts can quickly obtain the patient's historical and current physical condition information to formulate or adjust treatment plans.

[0040] S222: Based on the physical trend correlation data, through the formula: ; Calculate the patient's physical trend index to obtain the patient's physical change information; in, is the patient's physical tendency index, and is the weight factor, Indicates time point The physical fitness score, Indicates time point The instability index, is the time interval.

[0041] formula: ; Parameter details and how to obtain them : Weighting factor for the rate of change of physical fitness score. The value is usually set based on historical data analysis or expert opinion, with the goal of balancing the impact of changes in physical fitness score on the overall health trend in the assessment. For example, a higher The values ​​indicate that changes in physical fitness scores have a greater impact on health trends.

[0042] : Weighting factor for the rate of change of the instability index. The setting of the parameter is also based on the statistical analysis of past data or insights from clinical studies to ensure that changes in instability are appropriately considered in trend assessment.

[0043] and : Indicates at a point in time The physical fitness score of Indicates that at a previous point in time 's physical fitness score.

[0044] and : Indicates at a point in time The instability index is Represents the instability index at the previous time point.

[0045] : Indicates the time interval, i.e. arrive The length of time between the two dates can be days, weeks, or months.

[0046] Calculation example: Set the following data and parameters , , , , (one day), .

[0047] Calculate the rate of change of physical fitness score and the rate of change of instability index: ; ; calculate :: ; Calculation results Indicates from the time point arrive The combined changes in the physical fitness score and instability index affect health trends. The larger the value, the better the physical condition, while a smaller or negative value may indicate a worsening of health. This formula allows health experts to assess health dynamics in the short term and respond quickly to potential health problems.

[0048] See also Figure 5 , the steps to obtain the severity assessment results are: S311: extracting the infection type, number of types and duration data of the patient based on the patient's sepsis status detection data to obtain severity-related data; In the patient's sepsis test data, detailed information about the type of infection, the number of each type, and the duration of each infection is collected. The data is obtained through clinician evaluation and laboratory test results. Each type of infection such as bacteria, virus or fungus will be recorded. At the same time, the laboratory determines the specific type and number of infections through blood culture or other biomarker tests. The duration is calculated based on the medical records and the interval from the patient's first infection symptoms to the date of diagnosis. Accurate recording of data is crucial to assessing the patient's health status and its severity. After the data is collected, the medical team will enter the information into the patient's health record to form detailed severity-related data.

[0049] S312: Based on severity correlation data, by formula: ; Calculate the patient's severity score; in, Score the severity of the patient's is the number of infection types, for The duration of infection, For the The severity weight of each infection type; formula: ; Parameter details and how to obtain them: : represents the number of infection types, which is the number of different infection types identified through medical records. For example, if a patient has both bacterial and viral infections, will be 2.

[0050] :Indicates the The duration of an infection, usually measured in days, is obtained from the patient's medical record, from the time the infection was diagnosed to the time it responded to or ended treatment.

[0051] : No. The severity weight of each infection type is based on clinical research and historical data to assess the severity of different infection types. For example, bacterial infections may have a higher weight than common viral infections.

[0052] Calculation example: Assume the following data: Number of infection types Duration of infection sky, Days; infection severity weight , .

[0053] Calculate the adjustment factor for the number of infection types: ; Calculate the contribution of each infection: ; ; Calculating the total severity score :; ; score , indicating the severity of the patient's sepsis condition. According to the set severity level threshold (e.g., mild: 1-10, moderate: 11-20, severe: 21+), the patient's sepsis condition can be assessed as moderately severe. This method enables medical providers to quickly identify the severity of sepsis based on a quantitative score and formulate corresponding treatment strategies accordingly.

[0054] S313: Based on the severity score of the patient, the severity of the patient's sepsis condition is assessed according to the severity score to obtain a severity assessment result.

[0055] Based on the calculated severity score, doctors can more accurately assess the severity of a patient's sepsis. A higher severity score usually means that the patient's vital signs are unstable and may require more aggressive treatment or monitoring. The evaluation process includes comparison with previous similar cases and monitoring of current treatment responses. The score enables the medical team to develop targeted treatment plans, such as increasing the use of antibiotics, strengthening supportive care, or performing surgical intervention to ensure that patients receive the most appropriate treatment for their current condition, thereby improving treatment outcomes and survival rates.

[0056] See also Figure 6 , the steps to obtain the probability of worsening of sepsis are: S411: Based on the patient's physical fitness assessment results, the patient's physical fitness change information and the severity assessment results, the treatment records of similar sepsis patients are extracted, the average deterioration probability of similar sepsis patients under differentiated treatment measures is analyzed, and the average physical fitness trend index, average physical fitness score and average severity score of similar patients are extracted to obtain deterioration probability association data; Based on the patient's physical fitness assessment results, physical fitness change information and severity assessment results, the staff retrieved the treatment records of similar sepsis patients through the medical information system, including the patient's treatment measures, deterioration probability, physical fitness score and severity score. The execution of this step requires the system to be able to effectively classify and screen a large amount of data to ensure that the extracted data is comparable and relevant. Through statistical analysis of the data, the average physical fitness score, average physical fitness trend index and average severity score are obtained, which provides a scientific basis for evaluating the condition of individual patients and ensures the targeted and effective treatment measures.

[0057] S412: Based on the deterioration probability correlation data, the formula: ; Calculate the probability of worsening of sepsis under target treatment measures and obtain the worsening probability assessment result; in, , and is the weight factor, is the average probability of deterioration of similar sepsis patients under target treatment measures, and are the current patient's physical fitness score and the average physical fitness score of the same group, and They are the current patient's physical fitness trend index and the average physical fitness trend index of the same group. and are the severity score of the current patient and the average severity score of the same group, The probability of worsening of sepsis.

[0058] formula: ; Parameter details and how to obtain them :The average probability of deterioration of similar sepsis patients under target treatment measures. The data is obtained through the hospital's medical record system or through clinical research, providing a benchmark value for subsequent calculations.

[0059] and : is the current patient's physical fitness score, calculated by the previous steps. It is the average constitution score calculated from a similar group of patients and provides a benchmark for comparison.

[0060] and : Represents the current patient's physical trend index, which is calculated through the previous steps. It is the corresponding group average physical trend index, which is obtained by analyzing data of similar cases.

[0061] and : is the current patient's severity score, calculated by the previous step. The mean severity score for this group of patients was used to provide a baseline for comparison.

[0062] , , :The weight factor is set based on demand to adjust the contribution of different deviations to the final deterioration probability.

[0063] Calculation example: The following data are set for calculation: , indicating that under the target treatment conditions, the average deterioration probability of similar sepsis patients is 20%, and the patient's physical fitness score , the average physical fitness score of the same category , the patient's physical tendency index , group average physical trend index , the patient's severity score , average severity score , weight factor , , .

[0064] calculate The deviation ratio ;calculate Deviation ;calculate The deviation ratio ; Plugging the values ​​into the formula: ; ; ; ; Calculation results , indicating that the probability of worsening of the patient under the current treatment measures is 20.446%, which is slightly higher than the average level. The results can help the medical team understand the patient's potential risk relative to the average situation and adjust the treatment strategy accordingly.

[0065] See also Figure 7 , the steps to obtain the prediction information of sepsis exacerbation are: S421: Based on the patient's physical fitness assessment results, the patient's physical fitness change information and the severity assessment results, and according to the treatment records of similar sepsis patients, extract the average physical fitness score, physical fitness trend index, severity score and average deterioration rate of similar sepsis patients to obtain deterioration trend associated data; Based on the patient's physical condition assessment results, patient physical condition change information and severity assessment results, the medical team extracted detailed treatment records of similar sepsis patients through the hospital's health information system, including each patient's physical condition score, physical condition trend index, severity score and its deterioration rate over time. The data are aggregated from records at different time points to ensure the timeliness and accuracy of the data. Through data comparison and analysis, the average rate of patient deterioration under different treatment methods can be observed. In addition, the team also compares these data with the data of current patients to estimate the patient's possible deterioration trend. This process not only helps doctors understand the patient's current condition changes, but also provides a scientific basis for formulating more effective treatment plans.

[0066] S422: Based on the deterioration trend correlation data, the formula: ; Calculate the patient's sepsis exacerbation trend index; in, is the patient's sepsis worsening trend index, is the average deterioration rate of similar sepsis patients, and are the current patient's physical fitness score and the average physical fitness score of the same group, and They are the current patient's physical fitness trend index and the average physical fitness trend index of the same group. and are the severity score of the current patient and the average severity score of the same group, , and is the weight factor; formula: ; Parameter details and how to obtain them : The average deterioration rate of similar sepsis patients, usually based on historical data analysis. The parameter reflects the average deterioration trend of the same type of patients under standard treatment conditions.

[0067] and : is the current patient's physical fitness score, calculated by the previous steps. It is the average constitution score calculated from a similar group of patients and provides a benchmark for comparison.

[0068] and : Represents the current patient's physical trend index, which is calculated through the previous steps. It is the corresponding group average physical trend index, which is obtained by analyzing data of similar cases.

[0069] and : is the current patient's severity score, calculated by the previous step. The mean severity score for this group of patients was used to provide a baseline for comparison.

[0070] Weight Factor , , : Parameters set based on clinical data and expert opinion to adjust how different biases affect the calculation of worsening trend. Each weighting factor is adjusted for a specific scoring bias to ensure that the bias correctly reflects its importance in the worsening trend assessment.

[0071] Calculation example: The following parameters and data are set for calculation: , indicating that the average deterioration rate of sepsis in common situations is 50%.

[0072] , , indicating that the patient's physical fitness score is below average.

[0073] , , indicating that the patient's physical trend index is below average.

[0074] , , indicating that the patient's severity score is above average.

[0075] Weighting Factor: , , .

[0076] Plugging the values ​​into the formula: ; ; ; ; Calculation results The patient's sepsis worsening trend index is , which is higher than average, alerts the medical team that the patient is at higher risk of deterioration and may require more intensive or stricter monitoring and treatment measures.

[0077] S423: Based on the patient's sepsis deterioration trend index, the deterioration probability assessment results are integrated to obtain sepsis deterioration prediction information.

[0078] After calculating the patient's sepsis exacerbation trend index, the medical team integrates the data with the exacerbation probability assessment results, allowing doctors to obtain comprehensive disease prediction information that reflects the patient's overall disease trend and possible health risks. During the integration process, doctors take into account various physiological parameters and treatment responses to ensure the accuracy and practicality of the predictive information, which is crucial for formulating future treatment plans and patient monitoring strategies, and can help the medical team prepare in advance to deal with possible changes in the condition.

[0079] A sepsis deterioration prediction system based on TCM big data, the sepsis deterioration prediction system based on TCM big data is used to execute the above-mentioned sepsis deterioration prediction method based on TCM big data, the system comprises: The patient constitution analysis module extracts the TCM constitution and physiological index data of patients with sepsis. The TCM constitution data includes the patient's pulse and tongue condition, and the physiological index data includes the user's body temperature and blood index. The data is compared with the normal constitution data and standard physiological indexes provided by TCM big data. Based on the data deviation, the patient's physical condition is evaluated to obtain the patient's physical condition evaluation result. The instability analysis module is based on the patient's physical condition assessment results. By analyzing the fluctuations of the patient's TCM physical condition and physiological condition indicator data, the instability of the patient's condition is assessed to obtain the instability assessment results. The physical condition trend analysis module analyzes the changes in the physical condition and the instability of the condition of the patient over a period of time based on the physical condition assessment results and the instability assessment results of the patient, assesses the changing trend of the patient's physical condition, and obtains the patient's physical condition change information; The severity analysis module evaluates the severity of the patient's sepsis condition based on the patient's sepsis condition detection data and according to the patient's infection type, number of types and duration, and obtains a severity assessment result; The deterioration prediction module extracts the treatment records of similar sepsis patients based on the patient's physical assessment results, the patient's physical change information and the severity assessment results. According to the status data deviation, it analyzes the probability of sepsis deterioration under differentiated treatment measures for users, predicts the trend of sepsis deterioration, and obtains sepsis deterioration prediction information.

[0080] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for predicting the deterioration of sepsis based on big data of traditional Chinese medicine, characterized in that: The following steps are involved: Based on sepsis patients, the TCM constitution and physiological index data of the patients are extracted and compared with the normal constitution data and standard physiological indexes provided by TCM big data. According to the data deviation, the physical condition of the patients is evaluated to obtain the physical condition evaluation results of the patients. Based on the patient's physical condition assessment results, by analyzing the fluctuations of the patient's TCM physical condition and physiological condition indicator data, assessing the instability of the patient's condition, and analyzing the changes in the patient's physical condition and the instability of the condition over a period of time, assessing the changing trend of the patient's physical condition, and obtaining the patient's physical condition change information; Based on the patient's sepsis condition detection data, the severity of the patient's sepsis condition is evaluated according to the patient's infection type, number of types and duration, to obtain a severity evaluation result; Based on the patient's physical condition assessment results, the patient's physical condition change information and the severity assessment results, the treatment records of similar sepsis patients are extracted, and according to the status data deviation, the probability of sepsis deterioration under differentiated treatment measures is analyzed, and the trend of sepsis deterioration is predicted to obtain sepsis deterioration prediction information.

2. The method for predicting sepsis exacerbation based on TCM big data according to claim 1, characterized in that: The steps for obtaining the patient's physical fitness assessment result are: Based on sepsis patients, the TCM constitution and physiological index data of the patients are collected. The TCM constitution data includes the pulse and tongue of the patients, and the physiological index data includes the user's body temperature and blood indexes, so as to obtain the real-time status information of the patients; Based on the patient's real-time status information, extract standard values ​​of TCM constitution data and standard physiological index values ​​to obtain constitution-related data; Based on the constitution-related data, the formula: ; Calculate the patient's physical fitness score and obtain the patient's physical fitness assessment result; in, Score the patient's physical condition. and is the influence coefficient, For the The actual measured value of each TCM constitution data point, For the The standard value of each TCM constitution data point, For the The actual measured value of each physiological indicator data point, For the The standard physiological index value of each physiological index data point, For the Adjustment parameters, For the Adjustment parameters, For the The weight coefficients, For the The weight coefficients, is the total number of data points of TCM constitution, is the total number of data points of physiological indicators, is the base of natural logarithms.

3. The method for predicting sepsis exacerbation based on TCM big data according to claim 1, characterized in that: The steps of assessing the instability of the patient's condition are: Based on the patient's physical fitness assessment results, extract the patient's TCM physical fitness and physiological condition index data over a period of time, and calculate the average value of the data to obtain unstable correlation data; Based on the unstable correlation data, the formula: ; Calculate the patient's instability index; in, is the patient's instability index, and is the weight factor, For the The actual measured value of each TCM constitution data point, For the The actual measured value of each physiological indicator data point, is the average value of TCM constitution data. is the average value of physiological index data, is the total number of data points of physiological indicators, is the base of natural logarithms; Based on the instability index of the patient, the instability of the patient's condition is evaluated according to the numerical value of the instability index of the patient to obtain an instability evaluation result.

4. The method for predicting sepsis exacerbation based on TCM big data according to claim 3, characterized in that: The steps for obtaining the patient's physical condition change information are as follows: Based on the patient's physical fitness assessment results and instability assessment results, extract the patient's physical fitness score and instability index over a period of time, and record the corresponding time points to obtain physical fitness trend correlation data; Based on the physical trend correlation data, the formula: ； Calculate the patient's physical trend index to obtain the patient's physical change information; in, is the patient's physical tendency index, and is the weight factor, Indicates time point The physical fitness score, Indicates time point The instability index, is the time interval.

5. The method for predicting sepsis exacerbation based on TCM big data according to claim 1, characterized in that: The steps for obtaining the severity assessment result are: Based on the patient's sepsis status detection data, extract the patient's infection type, number of types and duration data to obtain severity-related data; Based on the severity correlation data, by the formula: ； Calculate the patient's severity score; in, Score the severity of the patient's is the number of infection types, for The duration of infection, For the The severity weight of each infection type; Based on the severity score of the patient, the severity of the patient's sepsis condition is assessed according to the severity score to obtain a severity assessment result.

6. The method for predicting sepsis exacerbation based on TCM big data according to claim 1, characterized in that: The steps for obtaining the probability of worsening of sepsis are: Based on the patient's physical fitness assessment results, the patient's physical fitness change information and the severity assessment results, extracting treatment records of similar sepsis patients, analyzing the average deterioration probability of similar sepsis patients under differentiated treatment measures, and extracting the average physical fitness trend index, average physical fitness score and average severity score of similar patients to obtain deterioration probability association data; Based on the deterioration probability association data, by formula: ; Calculate the probability of worsening of sepsis under target treatment measures and obtain the worsening probability assessment result; in, , and is the weight factor, is the average probability of deterioration of similar sepsis patients under target treatment measures, and are the current patient's physical fitness score and the average physical fitness score of the same group, and They are the current patient's physical fitness trend index and the average physical fitness trend index of the same group. and are the severity score of the current patient and the average severity score of the same group, The probability of worsening of sepsis.

7. The method for predicting sepsis deterioration based on TCM big data according to claim 6, characterized in that: The steps for obtaining the sepsis exacerbation prediction information are as follows: Based on the patient's physical fitness assessment results, the patient's physical fitness change information and the severity assessment results, and according to the treatment records of similar sepsis patients, extract the average physical fitness score, physical fitness trend index, severity score and average deterioration rate of similar sepsis patients to obtain deterioration trend associated data; Based on the deterioration trend correlation data, the formula: ; Calculate the patient's sepsis exacerbation trend index; in, is the patient's sepsis worsening trend index, is the average deterioration rate of similar sepsis patients, and are the current patient's physical fitness score and the average physical fitness score of the same group, and They are the current patient's physical fitness trend index and the average physical fitness trend index of the same group. and are the severity score of the current patient and the average severity score of the same group, , and is the weight factor; Based on the patient's sepsis deterioration trend index, the deterioration probability assessment results are integrated to obtain sepsis deterioration prediction information.

8. A sepsis deterioration prediction system based on TCM big data, characterized in that: According to any one of claims 1 to 7, the method for predicting sepsis deterioration based on TCM big data comprises: The patient constitution analysis module extracts the TCM constitution and physiological index data of patients with sepsis, compares them with the normal constitution data and standard physiological indexes provided by TCM big data, and evaluates the patient's constitution status according to the data deviation to obtain the patient's constitution evaluation result; The instability analysis module evaluates the instability of the patient's condition based on the patient's physical condition evaluation result by analyzing the fluctuation of the patient's TCM physical condition and physiological condition indicator data to obtain an instability evaluation result; The physical condition trend analysis module analyzes the physical condition changes and the instability changes of the patient over a period of time based on the physical condition assessment results and the instability assessment results of the patient, assesses the changing trend of the patient's physical condition, and obtains the patient's physical condition change information; The severity analysis module evaluates the severity of the patient's sepsis condition based on the patient's sepsis condition detection data and according to the patient's infection type, number of types and duration, and obtains a severity assessment result; The deterioration prediction module extracts the treatment records of similar sepsis patients based on the patient's physical fitness assessment results, the patient's physical fitness change information and the severity assessment results, analyzes the probability of sepsis deterioration under differentiated treatment measures for users according to the status data deviation, and predicts the trend of sepsis deterioration to obtain sepsis deterioration prediction information.

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