Intelligent chronic disease risk dynamic assessment method and system for old people

Abstract

The invention relates to the field of data processing, and particularly provides an intelligent chronic disease risk dynamic assessment method and system for old people, and the method comprises the steps: carrying out the risk assessment of basic physicochemical index data of a target old people individual through a preset risk assessment rule, so as to dynamically calculate a basic risk assessment score of the target old people individual in real time; the multiple chronic disease types are evaluated from a score-oriented angle to improve the overall evaluation efficiency, and then risk evaluation is performed on the comprehensive risk index data of the target elderly individual through the preset key value pair matching rule to perform comprehensive evaluation from a result-oriented angle, so that the limitation of single-oriented evaluation is avoided, and the evaluation efficiency is improved. And weighting refinement is carried out by comparing different guides so as to further improve the overall evaluation accuracy, and the efficiency and the accuracy of the chronic disease risk dynamic evaluation method are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to an intelligent dynamic assessment method and system for chronic disease risks in the elderly. Background Art

[0002] With the rapid development of data intelligence, data analysis and evaluation have been widely used in various scenarios, especially for chronic disease risk assessment of the elderly. Due to the complex data correlation and too many data correlation factors, the cost and difficulty of manual evaluation are high. Therefore, intelligent data analysis and evaluation methods have gradually become an important part of chronic disease risk assessment for the elderly.

[0003] In the existing technology, chronic disease risk assessment for the elderly mainly adopts a specific single chronic disease type assessment method, which sets specific data as the target in advance and avoids the problem of simultaneously processing multiple complex attribution factors by screening only relevant data. However, in actual assessment, a single target elderly individual usually has multiple chronic disease types. The existing method can only judge a single chronic disease type in one assessment, resulting in too low overall assessment efficiency. In addition, targeted assessment model parameter settings must be performed separately for each chronic disease type, resulting in high development and operation costs, which cannot meet the requirements of actual assessment.

[0004] Therefore, how to design an intelligent chronic disease risk assessment method for the elderly to meet the requirements of actual assessment is an urgent problem to be solved. Summary of the invention

[0005] Based on this, the purpose of the present invention is to provide an intelligent dynamic assessment method and system for chronic disease risks in the elderly. By pre-setting risk assessment rules, risk assessment is performed on the basic physical and chemical indicator data of the target elderly individuals, and the basic risk assessment scores of the target elderly individuals are calculated in real time and dynamically. Multiple chronic disease types are evaluated from a scoring-oriented perspective to improve the overall assessment efficiency. Then, risk assessment is performed on the comprehensive risk indicator data of the target elderly individuals through preset key-value pair matching rules, so as to perform a comprehensive assessment from a result-oriented perspective, avoiding the limitations of a single-oriented assessment, and then comparing different orientations for weighted refinement to further improve the accuracy of the overall assessment. The present invention improves the efficiency and accuracy of the dynamic assessment method for chronic disease risks.

[0006] The present invention proposes an intelligent dynamic assessment method for chronic disease risk in the elderly, comprising: Obtaining basic physical and chemical index data and comprehensive risk index data of the target individual from a preset individual index database and performing preprocessing, wherein the basic physical and chemical index data and comprehensive risk index data are both stored in the preset individual index database, and when obtaining the basic physical and chemical index data and comprehensive risk index data of the target individual, the preset individual index database is screened and captured according to the individual identity information; Determine a basic risk assessment score according to the basic physical and chemical indicator data and preset risk assessment rules, wherein the preset risk assessment rules are dynamic analytical expressions; Determine the comprehensive risk assessment result of the target individual according to the comprehensive risk indicator data and a preset key-value pair matching rule, wherein the preset key-value pair matching rule includes the comprehensive risk indicator data and the corresponding comprehensive risk assessment result, and when matching the comprehensive risk assessment result, perform data retrieval and data matching on the comprehensive risk assessment result value table according to the keywords in the comprehensive risk indicator data; If it is determined that there is a rough target item in the basic risk assessment score and the comprehensive risk assessment result, weighted refinement processing is performed on the rough target item according to a preset weighted refinement rule, wherein the preset weighted refinement rule determines weighted enhancement or refinement deletion in the weighted refinement processing according to the rough state of the rough target item; A final risk assessment report is then generated based on the basic risk assessment score and the comprehensive risk assessment result, and the final risk assessment report is saved in the risk assessment database and the weight is updated. When the final risk assessment report is saved in the risk assessment database, the risk assessment database is screened according to the type of chronic disease, and the weight of the risk assessment database is updated according to the indicator data change label of the target individual.

[0007] In summary, according to the above-mentioned intelligent dynamic assessment method for chronic disease risks of the elderly, risk assessment is performed on the basic physical and chemical indicator data of the target elderly individuals through preset risk assessment rules, so as to calculate the basic risk assessment scores of the target elderly individuals in real time and dynamically, and evaluate multiple chronic disease types from a scoring-oriented perspective to improve the overall assessment efficiency, and then risk assessment is performed on the comprehensive risk indicator data of the target elderly individuals through preset key-value pair matching rules, so as to perform a comprehensive assessment from a result-oriented perspective, avoiding the limitations of a single-oriented evaluation, and then comparing different orientations for weighted refinement to further improve the accuracy of the overall evaluation. The present invention improves the efficiency and accuracy of the dynamic assessment method for chronic disease risks. Specifically, the basic physicochemical indicator data and comprehensive risk indicator data of the target individual are obtained and preprocessed, and the basic risk assessment score is determined according to the basic physicochemical indicator data and the preset risk assessment rules. The preset risk assessment rules are dynamic analytical expressions. The basic risk assessment scores of the elderly target individuals are calculated dynamically and instantly through the preset risk assessment rules of the dynamic analytical expressions. Multiple chronic disease types are evaluated from a scoring-oriented perspective to improve the overall evaluation efficiency. The comprehensive risk assessment results of the target individual are determined according to the comprehensive risk indicator data and the preset key-value pair matching rules. The preset key-value pair matching rules include the comprehensive risk indicator data and the corresponding comprehensive risk assessment results, so as to conduct a comprehensive evaluation from a result-oriented perspective and avoid single-disease-oriented problems. The limitations of a guided assessment are eliminated, and the accuracy of the overall assessment is improved. If it is determined that there are rough target items in the basic risk assessment score and the comprehensive risk assessment results, the rough target items are weighted refined according to preset weighted refinement rules. The preset weighted refinement rules judge the weighted enhancement or refined deletion in the weighted refinement according to the rough state of the rough target items. The results of different guides are compared to enhance the rough assessment items between different guides, and the accuracy of the overall assessment is further improved. A final risk assessment report is generated according to the basic risk assessment score and the comprehensive risk assessment results, and the final risk assessment report is saved in the risk assessment database and the weight is updated. The present invention improves the efficiency and accuracy of the dynamic assessment method for chronic disease risks.

[0008] Furthermore, the step of obtaining the basic physical and chemical index data and comprehensive risk index data of the target individual and performing preprocessing specifically includes: After the target individual is tested for basic physical and chemical indicators, a preset individual indicator database is searched to see whether there is a historical basic physical and chemical indicator test record for the target individual; If there is no historical basic physical and chemical index test record for the target individual, the data obtained from this basic physical and chemical index test will be used as the basic physical and chemical index data of the target individual; If the target individual has a historical basic physical and chemical index test record, the data obtained from this basic physical and chemical index test is used as the basic physical and chemical index data of the target individual, and an index data change label is generated based on the historical basic physical and chemical index data obtained from the historical basic physical and chemical index test; After the comprehensive risk index detection of the target individual, the comprehensive risk index data is classified and obtained according to the data type in the preset individual index database to obtain comprehensive risk index data of multiple different data types, and the comprehensive risk index data is grouped and processed according to the digital floating point class and the text character class.

[0009] Furthermore, the step of generating the indicator data change label specifically includes: Compare the basic physical and chemical index data with the historical basic physical and chemical index data item by item to obtain a single or multiple different index data change items and the index data change values ​​of the corresponding index data change items; Screening the key change items and non-key change items among the change items of the indicator data; Determine whether the indicator data change value corresponding to the key change item is greater than or equal to a preset key change item change threshold; if the indicator data change value corresponding to the key change item is greater than or equal to the preset key change item change threshold, generate a key item heavy change label; if the indicator data change value corresponding to the key change item is less than the preset key change item change threshold, generate a key item light change label; Determine whether the indicator data change value corresponding to the non-critical change item is greater than or equal to a preset non-critical change item change threshold, and if the indicator data change value corresponding to the non-critical change item is greater than or equal to the preset non-critical change item change threshold, generate a non-critical item change label; The key item severe change label, the key item slight change label and the non-key item change label are inserted into the basic physical and chemical indicator data as indicator data change labels.

[0010] Furthermore, the step of determining the basic risk assessment score according to the basic physical and chemical indicator data and the preset risk assessment rules specifically includes: Divide the basic physical and chemical indicator data into evaluation groups according to preset risk assessment rules, each of the evaluation groups includes a plurality of the basic physical and chemical indicator data, and each of the basic physical and chemical indicator data has a unique corresponding evaluation group; Convert all basic physical and chemical index data in each of the evaluation groups into corresponding evaluation values, and calculate the evaluation score of the corresponding evaluation group according to the evaluation value, wherein the evaluation score calculation includes forward score calculation and inverse score calculation, wherein the evaluation value is forward accumulated in the forward score calculation to obtain the evaluation score, and the evaluation value is inverted and then accumulated in the inverse score calculation to obtain the evaluation score; Obtaining a basic risk assessment score according to the assessment scores of all the assessment groups; The assessment group includes a TCM constitution assessment group, and the TCM constitution assessment group includes qi deficiency and peaceful constitution items; The calculation rules for the evaluation scores corresponding to the Qi deficiency items are as follows: #qdc_2 + #qdc_3 + #qdc_4 + #qdc_14, Among them, #qdc represents a Qi deficiency item, and the digital label is a guide for the basic physical and chemical index data of the corresponding item. The evaluation score corresponding to the Qi deficiency item is calculated as a positive score calculation, and each Qi deficiency item is positively accumulated to obtain the TCM constitution [Qi deficiency] evaluation score; The rules for calculating the evaluation scores corresponding to the peace quality items are as follows: #bc_1 + (6 - #qdc_2) + (6 - #qdc_4) + (6 - #qsc_5) + (6 - #yadc_13), Among them, #bc represents a peaceful constitution item, #qsc represents a qi stagnation constitution item, #yadc represents a yang deficiency constitution item, 6 is the upper limit of the inverted score, and the evaluation score corresponding to the peaceful constitution item is calculated as an inverted score calculation. The items except the peaceful constitution item are inverted and then accumulated to obtain the TCM constitution [peaceful constitution] evaluation score.

[0011] Furthermore, the step of determining the comprehensive risk assessment result of the target individual according to the comprehensive risk indicator data and the preset key-value pair matching rule specifically includes: Extract keywords from the comprehensive risk indicator data according to preset key-value pair matching rules to obtain comprehensive risk variable names; Generate an index key according to the comprehensive risk variable name to search the comprehensive risk assessment result value table, wherein the index key is a single string that uniquely corresponds to the comprehensive risk assessment result value in the comprehensive risk assessment result value table; Retrieve and obtain a comprehensive risk assessment result value, and divide the comprehensive risk assessment result value into assessment groups according to a preset risk assessment rule, wherein each assessment group includes a plurality of comprehensive risk assessment result values, and each comprehensive risk assessment result value has a unique corresponding assessment group; An assessment result judgment is performed according to the comprehensive risk assessment result value, the assessment result judgment includes a first preset assessment result range threshold and a second assessment result range threshold, the first preset assessment result range threshold and the second assessment result range threshold are different values, and three different assessment result areas are formed according to the first preset assessment result range threshold and the second assessment result range threshold, each of the assessment result areas has a unique corresponding risk assessment result; Obtaining a comprehensive risk assessment result based on the risk assessment results of all the assessment groups; The evaluation group includes a TCM constitution evaluation group, and the TCM constitution evaluation group includes a Qi deficiency item. The preset key-value pair matching rule of the Qi deficiency item is as follows: (#qdcScore>=11)?1:(#qdcScore>8and#qdcScore<11)?2:(#qdcScore<=8)?4:'' Among them, #qdcScore represents the comprehensive risk assessment result value of the Qi deficiency item. In the preset key-value pair matching rules of the Qi deficiency item, when the comprehensive risk assessment result value of the Qi deficiency item is greater than or equal to the first preset assessment result range threshold, when the comprehensive risk assessment result value of the Qi deficiency item is less than the first preset assessment result range threshold and greater than the second preset assessment result range threshold, and when the comprehensive risk assessment result value of the Qi deficiency item is less than or equal to the second preset assessment result range threshold, the corresponding risk assessment results are obtained respectively. In the preset key-value pair matching rules of the Qi deficiency item, the first preset assessment result range threshold and the second preset assessment result range threshold are 11 and 8 respectively, and the guide characters of the risk assessment results in the preset key-value pair matching rules of the Qi deficiency item are 1, 2, and 4 respectively.

[0012] Furthermore, if it is determined that there are rough target items in the basic risk assessment score and the comprehensive risk assessment result, the step of weighted refinement of the rough target items according to a preset weighted refinement rule specifically includes: Make weighted refinement judgments based on the basic risk assessment scores and comprehensive risk assessment results; Determining whether there are any items in the basic risk assessment score that deviate from the comprehensive risk assessment result; if it is determined that there are any items in the basic risk assessment score that deviate from the comprehensive risk assessment result, performing a refinement process according to the comprehensive risk assessment result, wherein the refinement process includes deleting or replacing data according to the assessment result value in the comprehensive risk assessment result; Determine whether there is an offset item in the basic risk assessment score from the comprehensive risk assessment result. If it is determined that there is an offset item in the basic risk assessment score from the comprehensive risk assessment result, perform weighted processing according to the comprehensive risk assessment result and the indicator data change label in the basic physical and chemical indicator data. The weighted processing includes calculating the offset weight according to the indicator data change label to obtain the offset weight to weight the assessment result value of the comprehensive risk assessment result corresponding to the offset item, so as to obtain the offset-corrected basic risk assessment score.

[0013] Furthermore, the step of generating a final risk assessment report based on the basic risk assessment score and the comprehensive risk assessment result specifically includes: Calculate the chronic disease risk probability of the target individual according to the basic risk assessment score, and then search the corresponding doctor-side feedback text in the preset chronic disease database according to the chronic disease risk probability to generate a doctor-side feedback report; According to the comprehensive risk assessment results, the corresponding patient-side suggestion text is retrieved in the preset chronic disease database, and the text is filled according to the preset assessment report text template to generate a comprehensive risk assessment feedback report; According to the doctor-side feedback report and the comprehensive risk assessment feedback report, a final risk assessment report is obtained, and the basic risk assessment score and the comprehensive risk assessment result corresponding to the final risk assessment report are saved in a preset chronic disease database, and the indicator data change label of the target individual is updated.

[0014] The present invention proposes an intelligent dynamic assessment system for chronic disease risk in the elderly, comprising: A preprocessing module, used to obtain and preprocess the basic physical and chemical index data and comprehensive risk index data of the target individual, wherein the basic physical and chemical index data and comprehensive risk index data are both stored in a preset individual index database; A basic risk assessment module, used to determine a basic risk assessment score according to the basic physical and chemical indicator data and a preset risk assessment rule, wherein the preset risk assessment rule is a dynamic analytical expression; A comprehensive risk assessment module, used to determine the comprehensive risk assessment result of the target individual according to the comprehensive risk indicator data and a preset key-value pair matching rule, wherein the preset key-value pair matching rule includes the comprehensive risk indicator data and the corresponding comprehensive risk assessment result; A weighted refinement module, for, if it is determined that there is a rough target item in the basic risk assessment score and the comprehensive risk assessment result, performing a weighted refinement process on the rough target item according to a preset weighted refinement rule, wherein the preset weighted refinement rule determines weighted enhancement or refinement deletion in the weighted refinement process according to the rough state of the rough target item; The report generation module is used to generate a final risk assessment report based on the basic risk assessment score and the comprehensive risk assessment result, save the final risk assessment report to the risk assessment database and update the weight.

[0015] The present invention also provides a storage medium, which stores one or more programs, and when the programs are executed by a processor, they implement the above-mentioned intelligent dynamic assessment method for chronic disease risks in the elderly.

[0016] The present invention also provides a computer device, the computer device comprising a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, it implements the above-mentioned intelligent dynamic assessment method for chronic disease risks in the elderly. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of the intelligent dynamic assessment method for chronic disease risk in the elderly proposed in the first embodiment of the present invention; Figure 2 This is a flow chart of the intelligent dynamic assessment method for chronic disease risk in the elderly proposed in the second embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the intelligent dynamic assessment system for chronic disease risks in the elderly proposed in the third embodiment of the present invention.

[0018] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0019] In order to facilitate understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are provided in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0020] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0022] See also Figure 1 , see Figure 1 , which is a flow chart of an intelligent dynamic assessment method for chronic disease risks of the elderly proposed in the first embodiment of the present invention, and the intelligent dynamic assessment method for chronic disease risks of the elderly includes steps S01 to S05, wherein: Step S01: Obtain basic physical and chemical index data and comprehensive risk index data of the target individual and perform preprocessing; It should be noted that in this embodiment, both the basic physical and chemical index data and the comprehensive risk index data are stored in a preset individual index database, which is classified based on individuals. The data of each individual classification includes the records of the individual's current index data and historical index data. After the basic physical and chemical index test is performed on the target individual, the preset individual index database is searched to see whether there is a historical basic physical and chemical index test record for the target individual. If there is no historical basic physical and chemical index test record for the target individual, the data obtained from this basic physical and chemical index test will be used as the basic physical and chemical index data of the target individual; If the target individual has a historical basic physical and chemical index test record, the data obtained from this basic physical and chemical index test is used as the basic physical and chemical index data of the target individual, and an index data change label is generated based on the historical basic physical and chemical index data obtained from the historical basic physical and chemical index test; After the comprehensive risk index of the target individual is detected, the comprehensive risk index data is classified and obtained according to the data type in the preset individual index database to obtain comprehensive risk index data of multiple different data types, and the comprehensive risk index data is grouped and processed according to the digital floating point class and the text character class; In this embodiment, the steps of generating the indicator data change label specifically include: Compare the basic physical and chemical index data with the historical basic physical and chemical index data item by item to obtain a single or multiple different index data change items and the index data change values ​​of the corresponding index data change items; Screening the key change items and non-key change items among the indicator data change items; Determine whether the indicator data change value corresponding to the key change item is greater than or equal to a preset key change item change threshold; if the indicator data change value corresponding to the key change item is greater than or equal to the preset key change item change threshold, generate a key item heavy change label; if the indicator data change value corresponding to the key change item is less than the preset key change item change threshold, generate a key item light change label; Determine whether the indicator data change value corresponding to the non-critical change item is greater than or equal to a preset non-critical change item change threshold, and if the indicator data change value corresponding to the non-critical change item is greater than or equal to the preset non-critical change item change threshold, generate a non-critical item change label; The key item severe change label, the key item slight change label and the non-key item change label are inserted into the basic physical and chemical indicator data as indicator data change labels.

[0023] Step S02: Determine a basic risk assessment score based on basic physical and chemical indicator data and preset risk assessment rules; It should be noted that in this embodiment, the basic physical and chemical indicator data are divided into evaluation groups according to the preset risk assessment rules, each of the evaluation groups includes a plurality of the basic physical and chemical indicator data, and each of the basic physical and chemical indicator data has a unique corresponding evaluation group; Convert all basic physical and chemical index data in each of the evaluation groups into corresponding evaluation values, and calculate the evaluation score of the corresponding evaluation group according to the evaluation value, wherein the evaluation score calculation includes forward score calculation and inverse score calculation, wherein the evaluation value is forward accumulated in the forward score calculation to obtain the evaluation score, and the evaluation value is inverted and then accumulated in the inverse score calculation to obtain the evaluation score; A basic risk assessment score is obtained according to the assessment scores of all the assessment groups.

[0024] Step S03: Determine the comprehensive risk assessment result of the target individual according to the comprehensive risk indicator data and the preset key-value pair matching rules; It should be noted that in this embodiment, the comprehensive risk indicator data is subjected to keyword extraction according to a preset key-value pair matching rule to obtain a comprehensive risk variable name; Generate an index key according to the comprehensive risk variable name to search the comprehensive risk assessment result value table, wherein the index key is a single string that uniquely corresponds to the comprehensive risk assessment result value in the comprehensive risk assessment result value table; Retrieve and obtain a comprehensive risk assessment result value, and divide the comprehensive risk assessment result value into assessment groups according to a preset risk assessment rule, wherein each assessment group includes a plurality of comprehensive risk assessment result values, and each comprehensive risk assessment result value has a unique corresponding assessment group; An assessment result judgment is performed according to the comprehensive risk assessment result value, the assessment result judgment includes a first preset assessment result range threshold and a second assessment result range threshold, the first preset assessment result range threshold and the second assessment result range threshold are different values, and three different assessment result areas are formed according to the first preset assessment result range threshold and the second assessment result range threshold, each of the assessment result areas has a unique corresponding risk assessment result; A comprehensive risk assessment result is obtained based on the risk assessment results of all the assessment groups.

[0025] Step S04: if it is determined that there are rough target items in the basic risk assessment score and the comprehensive risk assessment result, the rough target items are weighted refined according to a preset weighted refinement rule; It should be noted that in this embodiment, weighted refinement judgment is performed based on the basic risk assessment score and the comprehensive risk assessment result; Determining whether there are any items in the basic risk assessment score that deviate from the comprehensive risk assessment result; if it is determined that there are any items in the basic risk assessment score that deviate from the comprehensive risk assessment result, performing a refinement process according to the comprehensive risk assessment result, wherein the refinement process includes deleting or replacing data according to the assessment result value in the comprehensive risk assessment result; Determine whether there is an offset item in the basic risk assessment score from the comprehensive risk assessment result. If it is determined that there is an offset item in the basic risk assessment score from the comprehensive risk assessment result, perform weighted processing according to the comprehensive risk assessment result and the indicator data change label in the basic physical and chemical indicator data. The weighted processing includes calculating the offset weight according to the indicator data change label to obtain the offset weight to weight the assessment result value of the comprehensive risk assessment result corresponding to the offset item, so as to obtain the offset-corrected basic risk assessment score.

[0026] Step S05: Generate a final risk assessment report based on the basic risk assessment score and the comprehensive risk assessment result, save the final risk assessment report to the risk assessment database and update the weight; It should be noted that in this embodiment, the chronic disease risk probability of the target individual is calculated according to the basic risk assessment score, and then the corresponding doctor-side feedback text is retrieved in the preset chronic disease database according to the chronic disease risk probability to generate a doctor-side feedback report; According to the comprehensive risk assessment results, the corresponding patient-side suggestion text is retrieved in the preset chronic disease database, and the text is filled according to the preset assessment report text template to generate a comprehensive risk assessment feedback report; According to the doctor-side feedback report and the comprehensive risk assessment feedback report, a final risk assessment report is obtained, and the basic risk assessment score and the comprehensive risk assessment result corresponding to the final risk assessment report are saved in a preset chronic disease database, and the indicator data change label of the target individual is updated.

[0027] In summary, according to the above-mentioned intelligent dynamic assessment method for chronic disease risks of the elderly, risk assessment is performed on the basic physical and chemical indicator data of the target elderly individuals through preset risk assessment rules, so as to calculate the basic risk assessment scores of the target elderly individuals in real time and dynamically, and evaluate multiple chronic disease types from a scoring-oriented perspective to improve the overall assessment efficiency, and then risk assessment is performed on the comprehensive risk indicator data of the target elderly individuals through preset key-value pair matching rules, so as to perform a comprehensive assessment from a result-oriented perspective, avoiding the limitations of a single-oriented evaluation, and then comparing different orientations for weighted refinement to further improve the accuracy of the overall evaluation. The present invention improves the efficiency and accuracy of the dynamic assessment method for chronic disease risks. Specifically, the basic physicochemical indicator data and comprehensive risk indicator data of the target individual are obtained and preprocessed, and the basic risk assessment score is determined according to the basic physicochemical indicator data and the preset risk assessment rules. The preset risk assessment rules are dynamic analytical expressions. The basic risk assessment scores of the elderly target individuals are calculated dynamically and instantly through the preset risk assessment rules of the dynamic analytical expressions. Multiple chronic disease types are evaluated from a scoring-oriented perspective to improve the overall evaluation efficiency. The comprehensive risk assessment results of the target individual are determined according to the comprehensive risk indicator data and the preset key-value pair matching rules. The preset key-value pair matching rules include the comprehensive risk indicator data and the corresponding comprehensive risk assessment results, so as to conduct a comprehensive evaluation from a result-oriented perspective and avoid single-disease-oriented problems. The limitations of a guided assessment are eliminated, and the accuracy of the overall assessment is improved. If it is determined that there are rough target items in the basic risk assessment score and the comprehensive risk assessment results, the rough target items are weighted refined according to preset weighted refinement rules. The preset weighted refinement rules judge the weighted enhancement or refined deletion in the weighted refinement according to the rough state of the rough target items. The results of different guides are compared to enhance the rough assessment items between different guides, and the accuracy of the overall assessment is further improved. A final risk assessment report is generated according to the basic risk assessment score and the comprehensive risk assessment results, and the final risk assessment report is saved in the risk assessment database and the weight is updated. The present invention improves the efficiency and accuracy of the dynamic assessment method for chronic disease risks.

[0028] See also Figure 2 , which is a flow chart of a method for dynamically assessing the risk of chronic diseases in the elderly according to a second embodiment of the present invention, and includes steps S11 to S15, wherein: Step S11: after the target individual is tested for basic physical and chemical indicators, it is searched whether there is a historical basic physical and chemical indicator test record for the target individual. If the target individual has a historical basic physical and chemical indicator test record, the data obtained from this basic physical and chemical indicator test is used as the basic physical and chemical indicator data of the target individual, and an indicator data change label is generated based on the historical basic physical and chemical indicator data obtained from the historical basic physical and chemical indicator test. After the target individual is tested for comprehensive risk indicators, the comprehensive risk indicator data is classified and obtained according to the data type to obtain comprehensive risk indicator data of multiple different data types. Step S12: Divide the basic physical and chemical indicator data into assessment groups according to preset risk assessment rules, convert all basic physical and chemical indicator data in each assessment group into corresponding assessment values, calculate the assessment score of the corresponding assessment group according to the assessment value, and obtain the basic risk assessment score; It should be noted that the preset risk assessment rules in this embodiment are dynamic analytical expressions, which are guided by the assessment scores. Specifically, taking the TCM constitution assessment group as an example, the following assessment items are included: TCM Constitution [Qi Deficiency] Assessment Score Rules: #qdc_2 + #qdc_3 + #qdc_4 + #qdc_14, Among them, #qdc represents the Qi deficiency item, and the digital label is the guide symbol of the basic physical and chemical index data of the corresponding item; The evaluation score calculation in the above TCM constitution [Qi deficiency] evaluation score rule is a positive score calculation, and each Qi deficiency item is positively accumulated to obtain the TCM constitution [Qi deficiency] evaluation score; TCM Constitution [Balanced Constitution] Assessment Score Rules: #bc_1 + (6 - #qdc_2) + (6 - #qdc_4) + (6 - #qsc_5) + (6 - #yadc_13), Among them, #bc indicates a peaceful quality item, #qsc indicates a qi stagnation quality item, and #yadc indicates a yang deficiency quality item; The evaluation score calculation in the above-mentioned TCM constitution [peaceful constitution] evaluation score rule is an inverted score calculation. Non-target items, i.e., non-peaceful constitution items, are inverted and then accumulated to obtain the TCM constitution [peaceful constitution] evaluation score. The upper limit of the inverted score in this embodiment is 6.

[0029] Step S13: extract keywords from the comprehensive risk indicator data according to preset key-value pair matching rules to obtain comprehensive risk variable names, generate index keys according to the comprehensive risk variable names, search the comprehensive risk assessment result value table, retrieve and obtain comprehensive risk assessment result values, divide the comprehensive risk assessment result values ​​into assessment groups according to preset risk assessment rules, judge the assessment results according to the comprehensive risk assessment result values, and obtain comprehensive risk assessment results; It should be noted that in this embodiment, the comprehensive risk indicator data in the preset key-value pair matching rule is used as the index key, and the comprehensive risk assessment result is used as the matching value. Specifically, taking the TCM constitution assessment group as an example, it includes the following assessment items: Preset key-value pair matching rules for TCM constitution [Qi deficiency]: (#qdcScore>=11)?1:(#qdcScore>8and#qdcScore<11)?2:(#qdcScore<=8)?4:'' Among them, #qdcScore represents the comprehensive risk assessment result value of the Qi deficiency item, specifically, when the comprehensive risk assessment result value of the Qi deficiency item is greater than or equal to the first preset assessment result range threshold, when the comprehensive risk assessment result value of the Qi deficiency item is less than the first preset assessment result range threshold and greater than the second preset assessment result range threshold, when the comprehensive risk assessment result value of the Qi deficiency item is less than or equal to the second preset assessment result range threshold, the corresponding risk assessment results are obtained respectively. In this embodiment, the first preset assessment result range threshold and the second preset assessment result range threshold are 11 and 8 respectively, and the guide characters of the risk assessment results in this embodiment are 1, 2, and 4 respectively; Taking the hypertension assessment group as an example, it includes the following assessment items: Hypertension [blood pressure value] preset key-value pair matching rules: (#sbp_num>= 180 or #dbp_num>=110) ? 5 : (#sbp_num>= 160 or #dbp_num>= 100) ? 4 : (#sbp_num>= 140 or #dbp_num>= 90) ? 3 : (#sbp_num>= 120 or #dbp_num>= 80) ? 2 :1 Among them, #sbp_num represents a high blood pressure item, and #dbp_num represents a low blood pressure item. In this embodiment, multiple pairs of preset evaluation result range thresholds are set to determine the evaluation results corresponding to the evaluation items. Specifically, when the high blood pressure item is greater than or equal to the first preset evaluation result range threshold or the low blood pressure item is greater than or equal to the second preset evaluation result range threshold, the corresponding risk assessment result is obtained. The first preset evaluation result range threshold and the second preset evaluation result range threshold in this embodiment are 180 and 110 pairs, 160 and 100 pairs, 140 and 90 pairs, 120 and 80 pairs, and a null pair, respectively, wherein the null pair represents other thresholds that are not the preset evaluation result range thresholds. The guide characters of the risk assessment results in this embodiment are 1, 2, 3, 4, and 5, wherein guide character 1 corresponds to normal blood pressure, guide character 2 corresponds to slightly high blood pressure, guide character 3 corresponds to grade 1 hypertension, guide character 4 corresponds to grade 2 hypertension, and guide character 5 corresponds to grade 3 hypertension.

[0030] Step S14: weighted refinement judgment is performed according to the basic risk assessment score and the comprehensive risk assessment result. If it is determined that there is a deviation item from the comprehensive risk assessment result in the basic risk assessment score, then refinement processing is performed according to the comprehensive risk assessment result. If it is determined that there is a deviation item from the comprehensive risk assessment result in the basic risk assessment score, then weighted processing is performed according to the comprehensive risk assessment result and the indicator data change label in the basic physical and chemical indicator data; Step S15: Calculate the chronic disease risk probability of the target individual according to the basic risk assessment score, and then retrieve the corresponding doctor-side feedback text in the preset chronic disease database according to the chronic disease risk probability to generate a doctor-side feedback report, and retrieve the corresponding patient-side suggestion text in the preset chronic disease database according to the comprehensive risk assessment result, and fill in the text according to the preset assessment report text template to generate a comprehensive risk assessment feedback report, obtain the final risk assessment report according to the doctor-side feedback report and the comprehensive risk assessment feedback report, and save the basic risk assessment score and comprehensive risk assessment result corresponding to the final risk assessment report to the preset chronic disease database, and update the indicator data change label of the target individual.

[0031] In summary, according to the above-mentioned intelligent dynamic assessment method for chronic disease risks of the elderly, risk assessment is performed on the basic physical and chemical indicator data of the target elderly individuals through preset risk assessment rules, so as to calculate the basic risk assessment scores of the target elderly individuals in real time and dynamically, and evaluate multiple chronic disease types from a scoring-oriented perspective to improve the overall assessment efficiency, and then risk assessment is performed on the comprehensive risk indicator data of the target elderly individuals through preset key-value pair matching rules, so as to perform a comprehensive assessment from a result-oriented perspective, avoiding the limitations of a single-oriented evaluation, and then comparing different orientations for weighted refinement to further improve the accuracy of the overall evaluation. The present invention improves the efficiency and accuracy of the dynamic assessment method for chronic disease risks. Specifically, the basic physicochemical indicator data and comprehensive risk indicator data of the target individual are obtained and preprocessed, and the basic risk assessment score is determined according to the basic physicochemical indicator data and the preset risk assessment rules. The preset risk assessment rules are dynamic analytical expressions. The basic risk assessment scores of the elderly target individuals are calculated dynamically and instantly through the preset risk assessment rules of the dynamic analytical expressions. Multiple chronic disease types are evaluated from a scoring-oriented perspective to improve the overall evaluation efficiency. The comprehensive risk assessment results of the target individual are determined according to the comprehensive risk indicator data and the preset key-value pair matching rules. The preset key-value pair matching rules include the comprehensive risk indicator data and the corresponding comprehensive risk assessment results, so as to conduct a comprehensive evaluation from a result-oriented perspective and avoid single-disease-oriented problems. The limitations of a guided assessment are eliminated, and the accuracy of the overall assessment is improved. If it is determined that there are rough target items in the basic risk assessment score and the comprehensive risk assessment results, the rough target items are weighted refined according to preset weighted refinement rules. The preset weighted refinement rules judge the weighted enhancement or refined deletion in the weighted refinement according to the rough state of the rough target items. The results of different guides are compared to enhance the rough assessment items between different guides, and the accuracy of the overall assessment is further improved. A final risk assessment report is generated according to the basic risk assessment score and the comprehensive risk assessment results, and the final risk assessment report is saved in the risk assessment database and the weight is updated. The present invention improves the efficiency and accuracy of the dynamic assessment method for chronic disease risks.

[0032] See also Figure 3 , which is a schematic diagram of the structure of an intelligent dynamic assessment system for chronic disease risks of the elderly proposed in the third embodiment of the present invention, and the system includes: A preprocessing module 10, used to obtain and preprocess the basic physical and chemical index data and comprehensive risk index data of the target individual, wherein the basic physical and chemical index data and comprehensive risk index data are both stored in a preset individual index database; A basic risk assessment module 20, for determining a basic risk assessment score according to the basic physical and chemical index data and a preset risk assessment rule, wherein the preset risk assessment rule is a dynamic analytical expression; A comprehensive risk assessment module 30, for determining a comprehensive risk assessment result of the target individual according to the comprehensive risk indicator data and a preset key-value pair matching rule, wherein the preset key-value pair matching rule includes the comprehensive risk indicator data and the corresponding comprehensive risk assessment result; A weighted refinement module 40 is used for, if it is determined that there is a rough target item in the basic risk assessment score and the comprehensive risk assessment result, performing a weighted refinement process on the rough target item according to a preset weighted refinement rule, wherein the preset weighted refinement rule determines weighted enhancement or refinement deletion in the weighted refinement process according to the rough state of the rough target item; The report generation module 50 is used to generate a final risk assessment report according to the basic risk assessment score and the comprehensive risk assessment result, save the final risk assessment report to the risk assessment database and update the weight.

[0033] The present invention also proposes a computer storage medium on which one or more programs are stored, which, when executed by a processor, implement the above-mentioned intelligent dynamic assessment method for chronic disease risks in the elderly.

[0034] The present invention also proposes a computer device, comprising a memory and a processor, wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the above-mentioned intelligent dynamic assessment method for chronic disease risks in the elderly.

[0035] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain storage, communication, propagation or transmission of a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0036] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0037] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0038] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0039] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An intelligent method for dynamic assessment of chronic disease risk in the elderly, characterized in that: include: Obtaining basic physical and chemical index data and comprehensive risk index data of the target individual from a preset individual index database and performing preprocessing, wherein the basic physical and chemical index data and comprehensive risk index data are both stored in the preset individual index database, and when obtaining the basic physical and chemical index data and comprehensive risk index data of the target individual, the preset individual index database is screened and captured according to the individual identity information; Determine a basic risk assessment score according to the basic physical and chemical indicator data and preset risk assessment rules, wherein the preset risk assessment rules are dynamic analytical expressions; Determine the comprehensive risk assessment result of the target individual according to the comprehensive risk indicator data and a preset key-value pair matching rule, wherein the preset key-value pair matching rule includes the comprehensive risk indicator data and the corresponding comprehensive risk assessment result, and when matching the comprehensive risk assessment result, perform data retrieval and data matching on the comprehensive risk assessment result value table according to the keywords in the comprehensive risk indicator data; If it is determined that there is a rough target item in the basic risk assessment score and the comprehensive risk assessment result, weighted refinement processing is performed on the rough target item according to a preset weighted refinement rule, wherein the preset weighted refinement rule determines weighted enhancement or refinement deletion in the weighted refinement processing according to the rough state of the rough target item; A final risk assessment report is then generated based on the basic risk assessment score and the comprehensive risk assessment result, and the final risk assessment report is saved in the risk assessment database and the weight is updated. When the final risk assessment report is saved in the risk assessment database, the risk assessment database is screened according to the type of chronic disease, and the weight of the risk assessment database is updated according to the indicator data change label of the target individual.

2. The intelligent dynamic assessment method for chronic disease risk in the elderly according to claim 1 is characterized in that: The step of obtaining the basic physical and chemical index data and comprehensive risk index data of the target individual and performing preprocessing specifically includes: After the target individual is tested for basic physical and chemical indicators, a preset individual indicator database is searched to see whether there is a historical basic physical and chemical indicator test record for the target individual; If there is no historical basic physical and chemical index test record for the target individual, the data obtained from this basic physical and chemical index test will be used as the basic physical and chemical index data of the target individual; If the target individual has a historical basic physical and chemical index test record, the data obtained from this basic physical and chemical index test is used as the basic physical and chemical index data of the target individual, and an index data change label is generated based on the historical basic physical and chemical index data obtained from the historical basic physical and chemical index test; After the comprehensive risk index detection of the target individual, the comprehensive risk index data is classified and obtained according to the data type in the preset individual index database to obtain comprehensive risk index data of multiple different data types, and the comprehensive risk index data is grouped and processed according to the digital floating point class and the text character class.

3. The intelligent dynamic assessment method for chronic disease risk in the elderly according to claim 2 is characterized in that: The step of generating an indicator data change label specifically includes: Compare the basic physical and chemical index data with the historical basic physical and chemical index data item by item to obtain a single or multiple different index data change items and the index data change values ​​of the corresponding index data change items; Screening the key change items and non-key change items among the indicator data change items; Determine whether the indicator data change value corresponding to the key change item is greater than or equal to a preset key change item change threshold; if the indicator data change value corresponding to the key change item is greater than or equal to the preset key change item change threshold, generate a key item heavy change label; if the indicator data change value corresponding to the key change item is less than the preset key change item change threshold, generate a key item light change label; Determine whether the indicator data change value corresponding to the non-critical change item is greater than or equal to a preset non-critical change item change threshold, and if the indicator data change value corresponding to the non-critical change item is greater than or equal to the preset non-critical change item change threshold, generate a non-critical item change label; The key item severe change label, the key item slight change label and the non-key item change label are inserted into the basic physical and chemical indicator data as indicator data change labels.

4. The intelligent dynamic assessment method for chronic disease risk in the elderly according to claim 1 is characterized in that: The step of determining the basic risk assessment score according to the basic physical and chemical indicator data and the preset risk assessment rules specifically includes: Divide the basic physical and chemical indicator data into evaluation groups according to preset risk assessment rules, each of the evaluation groups includes a plurality of the basic physical and chemical indicator data, and each of the basic physical and chemical indicator data has a unique corresponding evaluation group; Convert all basic physical and chemical index data in each of the evaluation groups into corresponding evaluation values, and calculate the evaluation score of the corresponding evaluation group according to the evaluation value, wherein the evaluation score calculation includes forward score calculation and inverse score calculation, wherein the evaluation value is forward accumulated in the forward score calculation to obtain the evaluation score, and the evaluation value is inverted and then accumulated in the inverse score calculation to obtain the evaluation score; Obtaining a basic risk assessment score according to the assessment scores of all the assessment groups; The assessment group includes a TCM constitution assessment group, and the TCM constitution assessment group includes qi deficiency and peaceful constitution items; The calculation rules for the evaluation scores corresponding to the Qi deficiency items are as follows: #qdc_2 + #qdc_3 + #qdc_4 + #qdc_14, Among them, #qdc represents a Qi deficiency item, and the digital label is a guide for the basic physical and chemical index data of the corresponding item. The evaluation score corresponding to the Qi deficiency item is calculated as a positive score calculation, and each Qi deficiency item is positively accumulated to obtain the TCM constitution [Qi deficiency] evaluation score; The rules for calculating the evaluation scores corresponding to the peace quality items are as follows: #bc_1 + (6 - #qdc_2) + (6 - #qdc_4) + (6 - #qsc_5) + (6 - #yadc_13), Among them, #bc represents a peaceful constitution item, #qsc represents a qi stagnation constitution item, #yadc represents a yang deficiency constitution item, 6 is the upper limit of the inverted score, and the evaluation score corresponding to the peaceful constitution item is calculated as an inverted score calculation. The items except the peaceful constitution item are inverted and then accumulated to obtain the TCM constitution [peaceful constitution] evaluation score.

5. The intelligent dynamic assessment method for chronic disease risk in the elderly according to claim 1 is characterized in that: The step of determining the comprehensive risk assessment result of the target individual according to the comprehensive risk indicator data and the preset key-value pair matching rule specifically includes: Extract keywords from the comprehensive risk indicator data according to preset key-value pair matching rules to obtain comprehensive risk variable names; Generate an index key according to the comprehensive risk variable name to search the comprehensive risk assessment result value table, wherein the index key is a single string that uniquely corresponds to the comprehensive risk assessment result value in the comprehensive risk assessment result value table; Retrieve and obtain a comprehensive risk assessment result value, and divide the comprehensive risk assessment result value into assessment groups according to a preset risk assessment rule, wherein each assessment group includes a plurality of comprehensive risk assessment result values, and each comprehensive risk assessment result value has a unique corresponding assessment group; An assessment result judgment is performed according to the comprehensive risk assessment result value, the assessment result judgment includes a first preset assessment result range threshold and a second assessment result range threshold, the first preset assessment result range threshold and the second assessment result range threshold are different values, and three different assessment result areas are formed according to the first preset assessment result range threshold and the second assessment result range threshold, each of the assessment result areas has a unique corresponding risk assessment result; Obtaining a comprehensive risk assessment result based on the risk assessment results of all the assessment groups; The evaluation group includes a TCM constitution evaluation group, and the TCM constitution evaluation group includes a Qi deficiency item. The preset key-value pair matching rule of the Qi deficiency item is as follows: (#qdcScore>=11)?1:(#qdcScore>8and#qdcScore<11)?2:(#qdcScore<=8)?4:'' Among them, #qdcScore represents the comprehensive risk assessment result value of the Qi deficiency item. In the preset key-value pair matching rules of the Qi deficiency item, when the comprehensive risk assessment result value of the Qi deficiency item is greater than or equal to the first preset assessment result range threshold, when the comprehensive risk assessment result value of the Qi deficiency item is less than the first preset assessment result range threshold and greater than the second preset assessment result range threshold, and when the comprehensive risk assessment result value of the Qi deficiency item is less than or equal to the second preset assessment result range threshold, the corresponding risk assessment results are obtained respectively. In the preset key-value pair matching rules of the Qi deficiency item, the first preset assessment result range threshold and the second preset assessment result range threshold are 11 and 8 respectively, and the guide characters of the risk assessment results in the preset key-value pair matching rules of the Qi deficiency item are 1, 2, and 4 respectively.

6. The intelligent dynamic assessment method for chronic disease risk in the elderly according to claim 1 is characterized in that: If it is determined that there is a rough target item in the basic risk assessment score and the comprehensive risk assessment result, the step of weighted refinement of the rough target item according to a preset weighted refinement rule specifically includes: Make weighted refinement judgments based on the basic risk assessment scores and comprehensive risk assessment results; Determining whether there are any items in the basic risk assessment score that deviate from the comprehensive risk assessment result; if it is determined that there are any items in the basic risk assessment score that deviate from the comprehensive risk assessment result, performing a refinement process according to the comprehensive risk assessment result, wherein the refinement process includes deleting or replacing data according to the assessment result value in the comprehensive risk assessment result; Determine whether there is an offset item in the basic risk assessment score from the comprehensive risk assessment result. If it is determined that there is an offset item in the basic risk assessment score from the comprehensive risk assessment result, perform weighted processing according to the comprehensive risk assessment result and the indicator data change label in the basic physical and chemical indicator data. The weighted processing includes calculating the offset weight according to the indicator data change label to obtain the offset weight to weight the assessment result value of the comprehensive risk assessment result corresponding to the offset item, so as to obtain the offset-corrected basic risk assessment score.

7. The intelligent dynamic assessment method for chronic disease risk in the elderly according to claim 1 is characterized in that: The step of generating a final risk assessment report based on the basic risk assessment score and the comprehensive risk assessment result specifically includes: Calculate the chronic disease risk probability of the target individual according to the basic risk assessment score, and then search the corresponding doctor-side feedback text in the preset chronic disease database according to the chronic disease risk probability to generate a doctor-side feedback report; According to the comprehensive risk assessment results, the corresponding patient-side suggestion text is retrieved in the preset chronic disease database, and the text is filled according to the preset assessment report text template to generate a comprehensive risk assessment feedback report; According to the doctor-side feedback report and the comprehensive risk assessment feedback report, a final risk assessment report is obtained, and the basic risk assessment score and the comprehensive risk assessment result corresponding to the final risk assessment report are saved in a preset chronic disease database, and the indicator data change label of the target individual is updated.

8. An intelligent chronic disease risk dynamic assessment system for the elderly, characterized by: include: A preprocessing module, used to obtain basic physical and chemical index data and comprehensive risk index data of a target individual from a preset individual index database and perform preprocessing, wherein the basic physical and chemical index data and comprehensive risk index data are both stored in the preset individual index database. When obtaining the basic physical and chemical index data and comprehensive risk index data of a target individual, the preset individual index database is screened and captured according to the individual identity information; A basic risk assessment module, used to determine a basic risk assessment score according to the basic physical and chemical indicator data and a preset risk assessment rule, wherein the preset risk assessment rule is a dynamic analytical expression; A comprehensive risk assessment module, used to determine the comprehensive risk assessment result of the target individual according to the comprehensive risk indicator data and a preset key-value pair matching rule, wherein the preset key-value pair matching rule includes the comprehensive risk indicator data and the corresponding comprehensive risk assessment result, and when matching the comprehensive risk assessment result, data retrieval and data matching are performed on the comprehensive risk assessment result value table according to the keywords in the comprehensive risk indicator data; A weighted refinement module, for, if it is determined that there is a rough target item in the basic risk assessment score and the comprehensive risk assessment result, performing a weighted refinement process on the rough target item according to a preset weighted refinement rule, wherein the preset weighted refinement rule determines weighted enhancement or refinement deletion in the weighted refinement process according to the rough state of the rough target item; A report generation module is used to generate a final risk assessment report based on the basic risk assessment score and the comprehensive risk assessment result, save the final risk assessment report to the risk assessment database and update the weight. When the final risk assessment report is saved to the risk assessment database, the risk assessment database is screened according to the type of chronic disease, and the weight of the risk assessment database is updated according to the indicator data change label of the target individual.

9. A storage medium, characterized in that: The storage medium stores one or more programs, which, when executed by the processor, implement the intelligent dynamic assessment method for chronic disease risks in the elderly as described in any one of claims 1-7.

10. A computer device, characterized in that: The computer device comprises a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, it implements the intelligent dynamic assessment method for chronic disease risks in the elderly as described in any one of claims 1-7.

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