Cardiovascular case data processing method and system

Abstract

The invention discloses a cardiovascular case data processing method and system, and belongs to the technical field of medical data processing. The method is used for solving the technical problem that in an existing scheme, supervision verification in different aspects and self-adaptive dynamic optimization adjustment are not carried out on standard data of different evaluation indexes. Periodic supervision and processing marking are performed on risk change states corresponding to different target patients based on an existing risk assessment scheme, data processing in the aspects of risk assessment adjustment and risk assessment improvement is performed on the existing risk assessment scheme, and assessment index processing arrays corresponding to different assessment indexes and use state values are obtained. The use state values corresponding to the different evaluation indexes are processed and analyzed, and the different evaluation indexes of the existing risk evaluation scheme are adaptively and dynamically adjusted and improved according to the analysis result, so that supervision verification in different aspects and adaptive dynamic optimization adjustment on the standard data of the different evaluation indexes are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data processing, and in particular to a method and system for processing cardiovascular case data. Background Art

[0002] Cardiovascular case data refers to a collection of clinical and laboratory data related to cardiovascular diseases, which is usually used for diagnosis, treatment, research and prognosis assessment. Its specific content varies depending on the medical institution, research purpose or data source.

[0003] When implementing existing cardiovascular case data processing solutions, there is a process of combining the medical records of different patients with their different disease risk impact data for multidimensional assessment and analysis, and scoring and marking the patients' disease risks based on the results of the multidimensional assessment and analysis. However, when conducting the multidimensional assessment and analysis, there is no regulatory verification of different aspects of the standard data of different assessment indicators, nor is there adaptive dynamic optimization and adjustment, resulting in poor reliability of the processing and analysis of disease risks corresponding to the potential information mined by different patients, as well as poor autonomous optimization management effects. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for processing cardiovascular case data, which is used to solve the technical problem that the existing solutions do not perform different aspects of regulatory verification on standard data of different evaluation indicators and adaptive dynamic optimization and adjustment.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A method for processing cardiovascular case data, comprising: Monitor and statistically evaluate the risk assessment results of different target patients using existing risk assessment schemes, and use existing risk assessment schemes to obtain risk retrospective assessment results corresponding to different target patients according to preset monitoring cycles, supervise and classify risk change states based on the risk assessment results and risk retrospective assessment results of different target patients, and obtain risk marker data corresponding to different target patients; According to the risk marker data corresponding to different target patients, the existing risk assessment scheme is processed for risk assessment adjustment and improvement, and the evaluation indicator processing arrays and usage status values ​​corresponding to different evaluation indicators are obtained. The usage status values ​​corresponding to different evaluation indicators are processed and analyzed, and according to the analysis results, the different evaluation indicators of the existing risk assessment scheme are adaptively adjusted and improved dynamically.

[0006] Preferably, the initial assessment risk level in the risk assessment results corresponding to different target patients and the retrospective assessment risk level in the corresponding risk retrospective assessment results are obtained; The risk change status of different target patients is supervised and classified to obtain the first patient marker, the second patient marker or the third patient marker corresponding to the different target patients.

[0007] Preferably, if the initial assessment risk level of the target patient is the same as its retrospective assessment risk level, the target patient is marked as the first patient and a risk change normal flag with a value of 0 is associated with the target patient; If the target patient's initial assessment risk level is greater than its retrospective assessment risk level, the target patient is marked as the second patient and its associated risk change improvement indicator is -1; If the target patient's initial assessment risk level is lower than its retrospective assessment risk level, the target patient is marked as the third patient, and its associated value is 1 with a severe risk change indicator; The marks corresponding to different target patients and the associated identification values ​​are sorted and combined respectively to obtain the risk mark data corresponding to different target patients.

[0008] Preferably, the risk marker data corresponding to all target patients are obtained, and the total number of times that different evaluation indicators corresponding to all second patients are evaluated and used is counted and sorted to obtain a first evaluation indicator supervision sequence; Counting and sorting the total number of times all first patients are evaluated and used corresponding to different evaluation indicators to obtain a second evaluation indicator supervision sequence; The total number of times all third patients corresponding to different evaluation indicators are evaluated and used is counted and sorted to obtain the third evaluation indicator supervision sequence.

[0009] Preferably, when the third evaluation indicator supervision sequence is processed and analyzed according to the first evaluation indicator supervision sequence and the second evaluation indicator supervision sequence; Mark the first evaluation indicator in the first evaluation indicator supervision sequence as the target indicator, obtain the first position number a of the target indicator in the second evaluation indicator supervision sequence, and the second position number b of the target indicator in the third evaluation indicator supervision sequence; The target indicator is evaluated and used in a state analyzed according to the first position number and the second position number.

[0010] Preferably, if b=a=1, it is determined that the target indicator is consistent in the evaluated use state, and an evaluation use identifier with a value of 0 is associated; If b∈[1, a], the target indicator is judged to be in a normal state of evaluation and use, and an evaluation use flag with a value of 1 is associated; If b [1, a], it is determined that the target indicator is in an abnormal state of evaluation and use, and is associated with an evaluation usage flag with a value of 2.

[0011] Preferably, the evaluation use identifiers corresponding to different evaluation indicators in the first evaluation indicator supervision sequence are analyzed in sequence, and the indicator weights and evaluation use identifiers corresponding to the different evaluation indicators are sorted and combined to obtain evaluation indicator processing arrays corresponding to the different evaluation indicators; The different elements in the evaluation indicator processing arrays of different evaluation indicators are multiplied in turn to obtain the usage status values ​​corresponding to the different evaluation indicators.

[0012] Preferably, the usage status values ​​corresponding to different evaluation indicators are sorted and combined according to the order of indicator weights to obtain an evaluation indicator usage status sequence; The evaluation indicators are used to perform data analysis on the state sequence. If all elements in the evaluation indicator state sequence are 0, a risk assessment improvement instruction is generated and it is prompted that the existing risk assessment scheme has an assessment blind spot. At the same time, statistics and processing analysis of other potential information of all second patients are performed according to the risk assessment improvement instruction.

[0013] Preferably, if there is an element in the evaluation indicator usage status sequence that is not 0, a risk assessment adjustment instruction is generated, all elements with non-0 values ​​in the evaluation indicator usage status sequence are sorted and combined to obtain an evaluation indicator usage adjustment sequence, and risk assessment adjustments are performed on the evaluation indicators belonging to all elements in the evaluation indicator usage adjustment sequence.

[0014] A cardiovascular case data processing system, comprising: The patient risk change status supervision and processing module is used to monitor and count the risk assessment results of different target patients evaluated by the existing risk assessment scheme, and use the existing risk assessment scheme to obtain the risk retrospective assessment results corresponding to different target patients according to the preset monitoring cycle assessment, and supervise and classify the risk change status according to the risk assessment results and risk retrospective assessment results of different target patients to obtain the risk marker data corresponding to different target patients; The risk assessment implementation multi-dimensional supervision adjustment module is used to process the data in terms of risk assessment adjustment and improvement of the existing risk assessment scheme according to the risk marker data corresponding to different target patients, obtain the evaluation indicator processing arrays and usage status values ​​corresponding to different evaluation indicators, process and analyze the usage status values ​​corresponding to different evaluation indicators, and adaptively and dynamically adjust and improve different evaluation indicators of the existing risk assessment scheme according to the analysis results.

[0015] Compared with the existing solutions, the present invention achieves the following beneficial effects: The present invention periodically monitors and processes the risk change states corresponding to different target patients based on the existing risk assessment scheme, which can not only realize the digital representation of the risk change states of different target patients, but also provide reliable screening and classification data support for the dynamic adjustment and improvement analysis of different assessment indicators of the subsequent existing risk assessment scheme.

[0016] The present invention obtains evaluation indicator processing arrays and usage status values ​​corresponding to different evaluation indicators by performing data processing on risk assessment adjustment and risk assessment improvement of existing risk assessment schemes, processes and analyzes the usage status values ​​corresponding to different evaluation indicators, and adaptively and dynamically adjusts and improves different evaluation indicators of existing risk assessment schemes according to the analysis results, thereby realizing regulatory verification in different aspects and adaptive dynamic optimization and adjustment of standard data of different evaluation indicators, thereby improving the reliability of processing and analysis of disease risks corresponding to potential information mined by different patients and the effect of autonomous optimization management. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the accompanying drawings.

[0018] Figure 1 The present invention is a flowchart of a method for processing cardiovascular case data.

[0019] Figure 2 A flowchart of the steps for supervising and classifying the risk change status of different target patients in the present invention.

[0020] Figure 3 A flowchart of the steps of analyzing the evaluation usage identifiers corresponding to different evaluation indicators in the first evaluation indicator supervision sequence in the present invention.

[0021] Figure 4 The module block diagram of a cardiovascular case data processing system in the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Example 1: Figure 1 As shown, the present invention is a method for processing cardiovascular case data, comprising: Monitor and count the risk assessment results of different target patients evaluated by the existing risk assessment scheme, and use the existing risk assessment scheme to obtain the risk retrospective assessment results corresponding to different target patients according to the preset monitoring cycle assessment, supervise and classify the risk change status according to the risk assessment results and risk retrospective assessment results of different target patients, and obtain the risk marker data corresponding to different target patients; including: Obtaining the initial assessment risk level in the corresponding risk assessment results of different target patients and the retrospective assessment risk level in the corresponding risk retrospective assessment results; It should be noted that the target patients are specifically patients with cardiovascular diseases, and the risk levels of different target patients are analyzed and determined through the existing risk assessment scheme. A comprehensive score can be given based on the target patients' medical results and other disease impact data; Specifically, other disease impact data, including family genetic data, lifestyle data, and dietary habit data; Among them, family genetic data includes whether the immediate family members have cardiovascular disease, and the corresponding family genetic weight score is given according to the presence or absence of cardiovascular disease; Lifestyle data, including but not limited to smoking and alcohol abuse, with weighted scores for corresponding lifestyle aspects based on presence or absence; Dietary habit data, including but not limited to whether high fat intake, high sugar intake, and high salt intake are present, and a weight score is assigned to the corresponding dietary habit based on the presence or absence of these factors; The weight scores corresponding to different aspects can be determined by professionals in this field based on their work experience and existing cardiovascular disease impact data, and the specific values ​​are not limited; In addition, the preset monitoring cycle can implement targeted periodic review according to the initial assessment risk level corresponding to different target patients, and the unit is month. The specific value can be determined by professional and technical personnel in this field according to the existing review plan; at the same time, the assessment risk level and the corresponding assessment risk value range can be customized according to the application requirements of the actual application scenario, and are not limited here; like Figure 2 As shown, when monitoring and classifying different target patients according to their risk change status; If the target patient's initial assessment risk level is the same as its retrospective assessment risk level, the target patient is marked as the first patient and its associated risk change normal flag is set to 0; If the target patient's initial assessment risk level is greater than its retrospective assessment risk level, the target patient is marked as the second patient and its associated risk change improvement indicator is -1; If the target patient's initial assessment risk level is lower than its retrospective assessment risk level, the target patient is marked as the third patient, and its associated value is 1 with a severe risk change indicator; The marks corresponding to different target patients and the associated identification values ​​are sorted and combined respectively to obtain the risk mark data corresponding to the different target patients; In an embodiment of the present invention, the risk change states corresponding to different target patients are periodically supervised and marked based on the existing risk assessment scheme, which can not only realize the digital representation of the risk change states of different target patients, but also provide reliable screening and classification data support for the dynamic adjustment and improvement analysis of different assessment indicators of the subsequent existing risk assessment scheme.

[0024] According to the risk marker data corresponding to different target patients, the existing risk assessment scheme is processed in terms of risk assessment adjustment and risk assessment improvement, and the evaluation indicator processing array and usage status value corresponding to different evaluation indicators are obtained. The usage status values ​​corresponding to different evaluation indicators are processed and analyzed, and according to the analysis results, different evaluation indicators of the existing risk assessment scheme are dynamically adjusted and improved adaptively; including: Obtain risk marker data corresponding to all target patients, count the total number of times all second patients are evaluated and used for different evaluation indicators, and arrange the corresponding different evaluation indicators in descending order according to the total number of times they are evaluated and used, to obtain a first evaluation indicator supervision sequence; The evaluation indicator being evaluated and used means that the evaluation indicator is referenced to score the risk level of the second patient; Counting the total number of times all first patients are evaluated using different evaluation indicators, and arranging the corresponding different evaluation indicators in descending order according to the numerical values ​​of the total number of times evaluated, to obtain a second evaluation indicator supervision sequence; Count the total number of times all third patients are evaluated and used for different evaluation indicators, and arrange the corresponding different evaluation indicators in descending order according to the total number of times they are evaluated and used, to obtain a third evaluation indicator supervision sequence; In the embodiment of the present invention, by processing and statistics the data of different evaluation indicators being evaluated and used for different patients with early classification marks, the pertinence and reliability of subsequent analysis of different evaluation indicators can be effectively improved; When the third evaluation indicator supervision sequence is processed and analyzed according to the first evaluation indicator supervision sequence and the second evaluation indicator supervision sequence; It can be understood that the second patient has an associated value of -1, which indicates an improved risk change; the first patient has an associated value of 0, which indicates a normal risk change; and the third patient has an associated value of 1, which indicates a severe risk change. Therefore, the monitoring data of the second patient and the first patient need to be used to process and analyze the monitoring data of the third patient. like Figure 3 As shown, the first evaluation indicator in the first evaluation indicator supervision sequence is marked as the target indicator, and the first position number a of the target indicator in the second evaluation indicator supervision sequence and the second position number b of the target indicator in the third evaluation indicator supervision sequence are obtained; a and b are both positive integers; If b=a=1, the target indicator is determined to be consistent in its evaluation and use status, and an evaluation use flag with a value of 0 is associated; If b∈[1, a], the target indicator is judged to be in a normal state of evaluation and use, and an evaluation use flag with a value of 1 is associated; If b [1, a], it is determined that the target indicator is abnormally evaluated and used, and the evaluation usage flag with a value of 2 is associated; Analyze the evaluation use identifiers corresponding to different evaluation indicators in the first evaluation indicator supervision sequence in turn, and sort and combine the indicator weights and evaluation use identifiers corresponding to different evaluation indicators to obtain evaluation indicator processing arrays corresponding to different evaluation indicators; Multiply different elements in the evaluation indicator processing arrays of different evaluation indicators in turn to obtain usage status values ​​corresponding to different evaluation indicators; It should be noted that the usage status value is used to perform data processing and calculation from the weight of the evaluation indicator itself and the evaluated usage status to digitally represent the usage status corresponding to the evaluation indicator; the smaller the usage status value, the more normal the usage status of the evaluation indicator; The usage status values ​​corresponding to different evaluation indicators are sorted and combined according to the order of indicator weights to obtain the evaluation indicator usage status sequence; Perform data analysis on the evaluation indicator usage state sequence. If all elements in the evaluation indicator usage state sequence are 0, generate a risk assessment improvement instruction and prompt that the existing risk assessment scheme has an assessment blind spot. At the same time, perform statistics and processing analysis on other potential information of all second patients according to the risk assessment improvement instruction; It is necessary to explain that statistics and processing analysis of other potential information of all second patients are performed, including but not limited to data processing analysis of the impact of other diseases on all second patients, such as metabolic disease data, obesity data, psychological factor data, sleep disorder data, etc.; by performing statistics and processing analysis on other potential information, it is possible to dynamically supplement and improve the existing assessment indicators of the existing risk assessment scheme; If there are elements in the evaluation indicator usage status sequence that are not 0, a risk assessment adjustment instruction is generated, all elements in the evaluation indicator usage status sequence whose values ​​are not 0 are sorted and combined to obtain an evaluation indicator usage adjustment sequence, and risk assessment adjustments are performed on the evaluation indicators to which all elements in the evaluation indicator usage adjustment sequence belong; Among them, risk assessment adjustments are made, specifically including reducing the weight of large-value assessment indicators and increasing the weight of small-value assessment indicators; Large values ​​and small values ​​can be compared and divided according to the median values ​​of the indicator weights corresponding to all existing evaluation indicators; the specific values ​​of the reduction and increase of the indicator weights can be determined according to the application requirements of the actual application scenarios and the specific range of the existing indicator weights. The specific values ​​are not limited here.

[0025] In the embodiment of the present invention, by performing data processing on the risk assessment adjustment and risk assessment improvement aspects of the existing risk assessment scheme, evaluation indicator processing arrays and usage status values ​​corresponding to different evaluation indicators are obtained, the usage status values ​​corresponding to the different evaluation indicators are processed and analyzed, and the different evaluation indicators of the existing risk assessment scheme are adaptively dynamically adjusted and improved according to the analysis results, thereby achieving regulatory verification of different aspects of the standard data of different evaluation indicators and adaptive dynamic optimization and adjustment, thereby improving the reliability of processing and analysis of disease risks corresponding to potential information mined by different patients and the effect of autonomous optimization management.

[0026] Example 2: Figure 4 As shown, a cardiovascular case data processing system comprises: The patient risk change status supervision and processing module is used to monitor and count the risk assessment results of different target patients evaluated by the existing risk assessment scheme, and use the existing risk assessment scheme to obtain the risk retrospective assessment results corresponding to different target patients according to the preset monitoring cycle assessment, and supervise and classify the risk change status according to the risk assessment results and risk retrospective assessment results of different target patients to obtain the risk marker data corresponding to different target patients; The risk assessment implementation multi-dimensional supervision adjustment module is used to process the data in terms of risk assessment adjustment and improvement of the existing risk assessment scheme according to the risk marker data corresponding to different target patients, obtain the evaluation indicator processing arrays and usage status values ​​corresponding to different evaluation indicators, process and analyze the usage status values ​​corresponding to different evaluation indicators, and adaptively and dynamically adjust and improve different evaluation indicators of the existing risk assessment scheme according to the analysis results.

[0027] In addition, the formulas involved in the above are all dimensionless and numerical calculations. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and simulating it with simulation software.

[0028] In the several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the above-described embodiments of the invention are only illustrative, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0029] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0030] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0031] It is obvious to a person skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0032] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for processing cardiovascular case data, characterized in that: include: Monitor and statistically evaluate the risk assessment results of different target patients using existing risk assessment schemes, and use existing risk assessment schemes to obtain risk retrospective assessment results corresponding to different target patients according to preset monitoring cycles, supervise and classify risk change states based on the risk assessment results and risk retrospective assessment results of different target patients, and obtain risk marker data corresponding to different target patients; According to the risk marker data corresponding to different target patients, the existing risk assessment scheme is processed for risk assessment adjustment and improvement, and the evaluation indicator processing arrays and usage status values ​​corresponding to different evaluation indicators are obtained. The usage status values ​​corresponding to different evaluation indicators are processed and analyzed, and according to the analysis results, the different evaluation indicators of the existing risk assessment scheme are adaptively adjusted and improved dynamically.

2. A method for processing cardiovascular case data according to claim 1, characterized in that: Obtaining the initial assessment risk level in the corresponding risk assessment results of different target patients and the retrospective assessment risk level in the corresponding risk retrospective assessment results; The risk change status of different target patients is supervised and classified to obtain the first patient marker, the second patient marker or the third patient marker corresponding to the different target patients.

3. A method for processing cardiovascular case data according to claim 2, characterized in that: If the target patient's initial assessment risk level is the same as its retrospective assessment risk level, the target patient is marked as the first patient and its associated risk change normal flag is set to 0; If the target patient's initial assessment risk level is greater than its retrospective assessment risk level, the target patient is marked as the second patient and its associated risk change improvement indicator is -1; If the target patient's initial assessment risk level is lower than its retrospective assessment risk level, the target patient is marked as the third patient, and its associated value is 1 with a severe risk change indicator; The labels corresponding to different target patients and the associated identification values ​​are sorted and combined respectively to obtain the risk label data corresponding to different target patients.

4. A method for processing cardiovascular case data according to claim 3, characterized in that: Obtain risk marker data corresponding to all target patients, count and sort the total number of times all second patients corresponding to different evaluation indicators are evaluated and used, and obtain a first evaluation indicator supervision sequence; Counting and sorting the total number of times all first patients are evaluated and used corresponding to different evaluation indicators to obtain a second evaluation indicator supervision sequence; The total number of times all third patients corresponding to different evaluation indicators are evaluated and used is counted and sorted to obtain the third evaluation indicator supervision sequence.

5. A method for processing cardiovascular case data according to claim 4, characterized in that: When the third evaluation indicator supervision sequence is processed and analyzed according to the first evaluation indicator supervision sequence and the second evaluation indicator supervision sequence; Mark the first evaluation indicator in the first evaluation indicator supervision sequence as the target indicator, obtain the first position number a of the target indicator in the second evaluation indicator supervision sequence, and the second position number b of the target indicator in the third evaluation indicator supervision sequence; The target indicator is evaluated and used in a state analyzed according to the first position number and the second position number.

6. A method for processing cardiovascular case data according to claim 5, characterized in that: If b=a=1, the target indicator is determined to be consistent in its evaluation and use status, and an evaluation use flag with a value of 0 is associated; If b∈[1, a], the target indicator is judged to be in a normal state of evaluation and use, and an evaluation use flag with a value of 1 is associated; If b [1, a], it is determined that the target indicator is in an abnormal state of evaluation and use, and is associated with an evaluation usage flag with a value of 2.

7. A method for processing cardiovascular case data according to claim 6, characterized in that: Analyze the evaluation use identifiers corresponding to different evaluation indicators in the first evaluation indicator supervision sequence in turn, and sort and combine the indicator weights and evaluation use identifiers corresponding to different evaluation indicators to obtain evaluation indicator processing arrays corresponding to different evaluation indicators; Different elements in the evaluation indicator processing arrays of different evaluation indicators are multiplied in turn to obtain usage status values ​​corresponding to different evaluation indicators.

8. A method for processing cardiovascular case data according to claim 7, characterized in that: The usage status values ​​corresponding to different evaluation indicators are sorted and combined according to the order of indicator weights to obtain the evaluation indicator usage status sequence; The evaluation indicators are used to perform data analysis on the state sequence. If all elements in the evaluation indicator state sequence are 0, a risk assessment improvement instruction is generated and it is prompted that the existing risk assessment scheme has an assessment blind spot. At the same time, statistics and processing analysis of other potential information of all second patients are performed according to the risk assessment improvement instruction.

9. A method for processing cardiovascular case data according to claim 8, characterized in that: If there are elements in the evaluation indicator usage status sequence that are not 0, a risk assessment adjustment instruction is generated, and all elements with non-0 values ​​in the evaluation indicator usage status sequence are sorted and combined to obtain an evaluation indicator usage adjustment sequence, and risk assessment adjustments are performed on the evaluation indicators belonging to all elements in the evaluation indicator usage adjustment sequence.

10. A cardiovascular case data processing system, using a cardiovascular case data processing method according to any one of claims 1 to 9, characterized in that: include: The patient risk change status supervision and processing module is used to monitor and count the risk assessment results of different target patients evaluated by the existing risk assessment scheme, and use the existing risk assessment scheme to obtain the risk retrospective assessment results corresponding to different target patients according to the preset monitoring cycle assessment, and supervise and classify the risk change status according to the risk assessment results and risk retrospective assessment results of different target patients to obtain the risk marker data corresponding to different target patients; The risk assessment implementation multi-dimensional supervision adjustment module is used to process the data in terms of risk assessment adjustment and improvement of the existing risk assessment scheme according to the risk marker data corresponding to different target patients, obtain the evaluation indicator processing arrays and usage status values ​​corresponding to different evaluation indicators, process and analyze the usage status values ​​corresponding to different evaluation indicators, and adaptively and dynamically adjust and improve different evaluation indicators of the existing risk assessment scheme according to the analysis results.

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