Intelligent senile chronic disease co-disease management system

Abstract

The invention provides an intelligent management system for chronic diseases and common diseases of old people. The intelligent management system comprises an old people comprehensive evaluation module, a multidisciplinary collaborative diagnosis and treatment module, a multi-medication risk prediction management module, an intelligent chronic disease follow-up diagnosis management module and a health science popularization knowledge base module, the senile comprehensive assessment module inputs the personal chronic disease map of the patient into a risk factor prediction model for prediction, and outputs a comprehensive assessment result; the multidisciplinary cooperative diagnosis and treatment module is used for the first diagnosis / receiving diagnosis doctor to coordinate multidisciplinary doctors to perform cooperative diagnosis and treatment; the multi-medication risk prediction management module is used for giving comprehensive diagnosis, multi-medication management and risk prediction to the patient through collaborative diagnosis and treatment; the chronic disease intelligent follow-up diagnosis management module carries out follow-up visit tracking, health risk prediction and review prompting on the patient; and the health science knowledge base module matches and pushes corresponding health education knowledge. The accuracy and timeliness of senile chronic disease co-disease risk assessment prediction and management are improved, and a good auxiliary effect is achieved on improvement of the life quality of patients.

Description

Technical Field

[0001] The present invention relates to the field of medical information technology, and in particular to an intelligent management system for chronic comorbidities in the elderly. Background Art

[0002] According to the census data, the proportion of the elderly population over 60 years old continues to rise. As they age, the elderly will show pathological and physiological characteristics such as decreased body function, decreased organ function, low immune and cognitive function, impaired limb movement, and endocrine and metabolic disorders. When an organ undergoes acute changes, other organs are also affected. Therefore, the elderly often suffer from multiple diseases involving multiple organs at the same time. Not only that, the cumulative effect of various symptoms and injuries is more obvious, resulting in the phenomenon of multiple diseases in elderly patients, that is, "multimorbidity". "Multimorbidity" is referred to as "comorbidity". In 2008, WHO defined multimorbidity as "a state in which two or more chronic diseases coexist in the same patient", emphasizing the coexistence of multiple chronic diseases, from disease induction to state induction, and establishing the concept of multimorbidity from a broader perspective.

[0003] It is estimated that more than 75% of the elderly have more than one chronic disease, and about 15% of the population has comorbid chronic diseases. And comorbid chronic diseases in the elderly have become a prominent global problem, leading to greater medical needs, more medical service use and costs. Comorbid chronic diseases not only lead to imbalances in the individual physiological functions of patients and increase adverse drug events, but also affect the prognosis of the disease and increase the medical burden. The prevention and treatment of comorbid chronic diseases has become an urgent issue. Effective prediction and assessment of the future disease risks of patients with comorbid chronic diseases can enable doctors to intervene in advance and reduce the risk of related diseases, thereby preventing them from happening, which is of great significance.

[0004] At present, there is no unified standard for risk prediction and assessment models and management systems for chronic disease comorbidities in the elderly. Factors such as research subjects, risk factors, model construction methods and doctors' experience levels will have an impact on the management of chronic disease comorbidities in the elderly. How to comprehensively consider multi-dimensional data and the interactions between diseases and drugs is an urgent problem that needs to be solved to improve the precise management of chronic disease comorbidities in the elderly. Summary of the invention

[0005] Based on the defects and shortcomings of the prior art, the present invention provides an intelligent management system for comorbid chronic diseases in the elderly, which includes multi-dimensional data characteristics of patients, coordinates and brings together doctors from different departments to jointly consider the interactions between diseases and therapeutic drugs, improves the accuracy and timeliness of risk assessment, prediction and management of comorbid chronic diseases in the elderly, and plays a good auxiliary role in improving the quality of life of patients.

[0006] To achieve the above object, the technical solution of the present invention is as follows: An intelligent management system for chronic comorbidities in the elderly, including: a comprehensive assessment module for the elderly, a multidisciplinary collaborative diagnosis and treatment module, and a multiple medication risk prediction and management module; The comprehensive assessment module for the elderly constructs a personal chronic disease map for the elderly based on their personal information, diagnostic data, and medication information. The map is then input into the risk factor prediction model for prediction, and the health risk warning and comprehensive assessment results for the patient are output. The multidisciplinary collaborative diagnosis and treatment module is used by the first-visit / receiving doctor to coordinate multidisciplinary doctors to conduct collaborative diagnosis and treatment based on the comprehensive assessment results of the geriatric comprehensive assessment module; The multiple medication risk prediction management module is used for the first-visit / admitting physician to provide comprehensive diagnosis, multiple medication management and risk prediction to the patient through the collaborative diagnosis and treatment.

[0007] Furthermore, the comprehensive geriatric assessment module sets up a personal information database to store the patient's personal information, sets up a diagnostic test database to store the diagnostic data of the patient's visits and tests and all related examinations during hospitalization, and sets up a medication information database to store the patient's daily and hospitalized medication information.

[0008] Furthermore, the comprehensive elderly assessment module also includes: obtaining a scale score based on the patient's evaluation scale, inputting the patient's personal chronic disease map and scale score into a risk factor prediction model for prediction, and outputting health risk prompts and comprehensive assessment results for the patient.

[0009] Furthermore, the comprehensive geriatric assessment module is provided with an assessment scale database to store assessment scales covering physical function, geriatric syndrome, mental and psychological conditions, and the Charlson comorbidity index.

[0010] Furthermore, the risk factor prediction model is one or more of a multifactor logistic regression model, a Cox regression model, a neural network machine learning model, and a time series disease network model.

[0011] Furthermore, the multidisciplinary collaborative diagnosis and treatment module includes a multidisciplinary integrated triage platform and a multidisciplinary patient precision clinic; the multidisciplinary integrated triage platform includes the information of doctors in each department, and the first-visit / admitting doctor comprehensively selects the required consultation departments and relevant doctors based on the comprehensive evaluation results, and the above-mentioned relevant doctors jointly form a multidisciplinary patient precision clinic; the multidisciplinary patient precision clinic is a specialty related to the diagnosis and treatment of chronic diseases, including geriatrics, endocrinology and metabolism, cardiovascular medicine, respiratory medicine, gastroenterology, nephrology, neurology, neurosurgery, oncology, clinical psychology, clinical nutrition, rehabilitation medicine, pharmacy, and nursing department.

[0012] Furthermore, the multiple medication risk prediction management module includes comprehensive diagnostic testing, multiple medication management, and diet and daily life management functions.

[0013] Furthermore, multiple medication management follows the principles of non-drug treatment priority, benefit principle, five-drug principle, small dose principle, timing principle and medication suspension principle; the multiple medication management also includes AI medication warning to promptly alert patients of medication contraindications.

[0014] Furthermore, it also includes a smart follow-up management module for chronic diseases, which includes a follow-up tracking function, a reexamination reminder function and a risk factor reminder function, and performs follow-up tracking, health risk prediction and reexamination reminders for elderly patients with chronic diseases.

[0015] Furthermore, it also includes a health science knowledge base module, which includes a medical health education science knowledge base. The medical health education science knowledge base is dynamically updated, and according to the diagnosis results made by the multidisciplinary patient precision clinic, the corresponding health education knowledge is timely matched and pushed to the patients.

[0016] Compared with the prior art, the present invention has the following advantages: 1. This invention integrates all functional modules of the whole process management of chronic diseases in the elderly: (1) Comprehensive geriatric assessment: Establish a comprehensive geriatric assessment platform to fully understand the patient's health status through comprehensive assessment and provide a basis for subsequent management.

[0017] (2) Multidisciplinary team integrated management: Establish a multidisciplinary team integrated management team and platform to provide systematic, comprehensive, safe, convenient and effective medical services, and conduct comprehensive assessment, standardized diagnosis and treatment, and health guidance for elderly patients.

[0018] (3) Multiple medication management: Doctors will implement precise individualized medication management by giving priority to non-drug treatments, following the benefit principle, the five-drug principle, the low-dose principle, the timing principle, and the medication suspension principle.

[0019] (4) Health education and health guidance: including health guidance for the elderly on chronic disease risk factors and vaccination, osteoporosis prevention and fall prevention measures, accidental injuries and self-help.

[0020] (5) Chronic disease management: For chronic diseases such as hypertension and diabetes, the platform provides regular health guidance and full-process follow-up to improve the effectiveness of disease management and significantly improve the quality of life of patients.

[0021] 2. The present invention realizes the intelligent management of chronic comorbidities in the elderly through artificial intelligence, big data analysis and risk factor prediction models, which helps doctors to accurately intervene in chronic comorbidities in the elderly and implement full-process management. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a schematic diagram of the architecture of the intelligent management system for chronic comorbidities in the elderly according to Example 1 of the present invention.

[0023] Figure 2 It is a flowchart of the intelligent management system for chronic comorbidities in the elderly according to Example 1 of the present invention.

[0024] Figure 3 This is a schematic diagram of the doctor side of the intelligent management system for chronic comorbidities in the elderly according to Example 1 of the present invention.

[0025] Figure 4 This is a patient-side schematic diagram of the intelligent management system for chronic comorbidities in the elderly according to Example 1 of the present invention. DETAILED DESCRIPTION

[0026] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other, and the specific features, structures or characteristics described in combination with the embodiments can be included in at least one embodiment of the present application. Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present application; it should be noted that the conventional conditions and methods not described in detail in the embodiments are all carried out in accordance with the experimental conditions and methods conventionally used by people in the field.

[0027] Embodiment 1: As shown in Figure 1, the intelligent management system for chronic diseases in the elderly includes five system modules: (1) comprehensive assessment module for the elderly, (2) multidisciplinary collaborative diagnosis and treatment module, (3) multiple medication risk prediction management module, (4) intelligent follow-up management module for chronic diseases, and (5) health science knowledge base module. Data communication connections are established between each module.

[0028] The comprehensive assessment module for the elderly includes multiple databases. In this embodiment, a patient personal information database, a diagnostic test database, a medication information database, and an evaluation scale database are set up, and the relevant information therein can be retrieved. The risk factor prediction model is input based on the patient's previous medical history diagnosis, test and medication information, scale results, etc., and a comprehensive calculation and evaluation is performed to obtain a health value range displayed in various forms such as "mathematical formulas, scoring systems, forest plots, nomograms, etc."

[0029] In the multidisciplinary collaborative diagnosis and treatment module, the first-visit / admitting doctor coordinates multidisciplinary doctors to conduct collaborative diagnosis and treatment based on the initial evaluation results of the comprehensive geriatric assessment module; doctors from different departments jointly form a multidisciplinary patient precision clinic.

[0030] In the multiple medication risk prediction management module, doctors prescribe comprehensive diagnostic tests for patients through the collaborative diagnosis and treatment, avoiding repeated and redundant tests; through joint comprehensive judgment of the test results, patients are given a comprehensive and systematic comprehensive diagnosis and multiple medication management and guidance; if necessary, diet and daily life advice and corresponding management are also provided to patients.

[0031] After diagnosis and treatment, doctors can follow up on patients, predict health risks, and provide reexamination reminders through the intelligent follow-up management module for chronic diseases.

[0032] The health science knowledge base module is dynamically updated and can timely match and push corresponding health education knowledge to patients based on the diagnosis results made by the multidisciplinary patient precision clinic. The module also includes commonly used health education knowledge for the elderly, such as vaccination, osteoporosis prevention and fall prevention measures, accidental injuries and self-rescue, to help elderly patients and their families with daily management in a comprehensive, systematic and comprehensive manner.

[0033] As shown in Figure 2, the workflow diagram of the intelligent management system for chronic comorbidities in the elderly is shown. After the patient enters the system, he can enter and retrieve the database, establish basic data, and after calculation and analysis, enter the multidisciplinary integrated triage platform. The first-visit / receiving doctor will coordinate the establishment of a multidisciplinary patient precision clinic, which will issue diagnostic test notifications, medication management notifications, and diet and living reminders to manage multiple medication risks; each time the corresponding diagnostic test information, multiple medication information and other data are obtained, they will be automatically imported into the patient's basic database (personal information database, diagnostic test database, medication information database) for the patient to use / call the next time he logs into the system. After the patient completes the medical treatment, the doctor will follow up, reexamination reminders or risk factor reminders through the intelligent follow-up management module for chronic diseases, and can also push the corresponding information in the health science knowledge base module to the patient for learning and self-management.

[0034] As shown in Figure 3, this is an example of the contents of the doctor-side visual interface of the intelligent management system for chronic comorbidities in the elderly. The first interface contains various database ports, callable data and comprehensive evaluation results, which are visible to both doctors and patients. In the second interface, the first-visit / receiving doctor can see the doctor information and scheduling of each department in the hospital, and can select the corresponding department and doctor as needed for overall consultation; this interface is only visible to doctors, not patients. In the third interface, the first-visit / receiving physician selects relevant doctors to form a multidisciplinary patient precision clinic (for example, cardiologist 1, endocrinologist 2, nephrologist 3, etc.). In this interface, doctors and patients can see it together and can have conversations and information exchange. In the fourth interface, multidisciplinary doctors can choose to prescribe corresponding examinations for the patient's condition and push them to the patient. This interface is visible to both doctors and patients. In the fifth interface, multidisciplinary doctors discuss and exchange opinions, and finally form a comprehensive diagnosis result and corresponding medication guidance. This interface is only visible to doctors, not patients. The sixth interface is visible to both doctors and patients, and provides patients with diet, daily life, medication guidance, review suggestions and health education knowledge push. The seventh interface is the follow-up tracking interface, where doctors can issue medication reminders, review reminders, risk factor reminders, etc., and can provide timely health education guidance based on the patient's medication or physical sign data feedback. The risk factor prompt is based on the health value range obtained by the comprehensive assessment module of the elderly to distinguish whether it is a risk factor that needs attention.

[0035] As shown in Figure 4, this is an example of the content included in the patient-side visual interface of the intelligent management system for chronic comorbidities in the elderly. The first interface contains various database ports, callable data and comprehensive evaluation results. This interface is visible to both doctors and patients, and patients can enter, revise and confirm the information. In the second interface, a multidisciplinary patient precision clinic for the patient's condition has been established, which is visible to patients and multidisciplinary doctor teams and can communicate and communicate. In the third interface, patients can see the test push given by the doctor team and the relevant results and impact pictures after the test. In the fourth interface, patients can see the patient's diet, daily life, medication guidance, review suggestions and health education knowledge push given by the doctor team. In the fifth interface, patients can receive medication or review reminders pushed by doctors and the system after diagnosis and treatment, and can record medication and review; in special circumstances, they can also use the platform to consult with doctors in the Internet hospital, and can use the network platform connected to the hospital to make registration appointments. The intelligent management system for chronic comorbidities in the elderly can also be loaded on APP or other small programs on computers, mobile phones, and watches to promptly remind possible risk factors and provide convenient medical service channels that patients may choose.

[0036] Embodiment 2: This embodiment proposes a further detailed solution for the comprehensive assessment module for the elderly.

[0037] In this embodiment, the comprehensive assessment module for the elderly sets multiple databases and computing tools, which are specifically described as follows: The patient personal information database stores the patient's personal information, such as the patient's age, gender, education level, marital status, living pattern, smoking, drinking, body mass index, sleep conditions, past medical history, health self-assessment, medical insurance reimbursement method, etc.

[0038] The diagnostic test database stores record information of tests and all related examinations performed on patients during their visits and hospitalization; related examination records include data such as the patient's diagnosis time, test reports, pictures, and diagnostic results; the diagnostic test database may also include the patient's genetic test data. For example, the following imaging, biochemical and genetic testing indicators are collected to preliminarily build a diagnostic test database: blood pressure, electrocardiogram, echocardiogram, cardiac computed tomography, cardiac magnetic resonance imaging, coronary angiography, and cardiac angiography; biomarkers (B-type natriuretic peptide (BNP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP), troponin (cTn), matrix protein 2 (ST2) and soluble ST2 (sST2), galectin 3 (Gal-3), growth differentiation factor 15 (GDF-15), serum cystatin C (Cys C), neutrophil gelatinase transporter (NGAL)); five-item full blood examination of white blood cell count, red blood cell count, hemoglobin concentration, platelet count, clotting time, blood sugar, blood lipids, liver function, kidney function and other blood tests; genetic testing data related to cardiovascular disease pharmacogenomics.

[0039] The medication information database stores the daily medication use of patients and during hospitalization, including data on prescription drugs and over-the-counter drugs, but does not include health products. For example, the medication use of cardiovascular diseases (coronary heart disease (CHD), hypertension, arrhythmia, valvular heart disease, myocarditis / primary cardiomyopathy, congenital heart disease, pericardial disease, etc.), non-cardiovascular diseases (dyslipidemia, anemia, chronic kidney disease (CKD), type 2 diabetes, cerebrovascular disease (CVD), obesity, peripheral vascular disease (PVD), malignant tumors (including solid tumors, leukemia, lymphoma, etc.), chronic obstructive pulmonary disease (COPD), hyperthyroidism, hypothyroidism, etc.) can be collected.

[0040] The evaluation scale database includes comprehensive evaluation scales for the elderly, such as Activity of daily living (ADL), Frail Scale (FS), Pittsburgh Sleep Quality Index (PSQI), and Charlson co-morbidity index (CCI).

[0041] The operation program included in the comprehensive assessment module for the elderly includes a patient's personal chronic disease map and a risk factor prediction model. After the operation program obtains the patient's personal information and the patient's case and medication record information from the personal information database, the diagnostic test database, and the medication information database, the chronic diseases suffered by the patient are sequentially numbered based on the sorting results; according to the patient's personal characteristic information and the sequential numbering results, the patient's personal chronic disease map is constructed, the map includes a node set and an edge set, the node set includes patient nodes and chronic disease nodes, the edges in the edge set indicate that the patient is diagnosed with a chronic disease, and the order of diagnosis is coded as n, and n is a positive integer greater than or equal to 1, the patient's personal chronic disease map is constructed, the constructed patient's personal chronic disease map is input into the risk factor prediction model for prediction, and the patient's health risk prompt and comprehensive assessment results are output.

[0042] In addition, the operation program described in this embodiment can also obtain the scale score from the evaluation scale database as needed, and the scale score includes the patient's comorbidity index, daily living ability assessment scale score, patient frailty degree scale score, etc. Then the operation program inputs the constructed patient's personal chronic disease map and scale score into the risk factor prediction model for prediction, and outputs the patient's health risk prompt and comprehensive evaluation results. The scale score is a real-time assessment of the patient's status, which can supplement and correct the patient's personal chronic disease map, and can output a more accurate comprehensive evaluation result.

[0043] Risk factor prediction models include a variety of models, such as multivariate logistic regression and Cox regression, neural network machine learning models, time series disease network models, etc. The constructed risk prediction models can also be presented in different ways, such as mathematical formulas, scoring systems, forest plots, nomograms, etc.

[0044] Example 1 (multivariate logistic regression analysis): With in-hospital death as the dependent variable and 10 chronic diseases as independent variables, univariate and multivariate logistic regression analysis was performed to screen out four comorbidities, namely "chronic ischemic heart disease, hypertension, diabetes and cerebrovascular disease", as independent risk factors for in-hospital death in elderly patients with heart failure. On this basis, age stratification, New York Heart Association (NYHA) grade ≥ grade III and N-terminal Pro-Brain Natriuretic Peptide (NT-proBNP) > 3000ng / L, three indicators related to death or the severity of heart failure, were added as independent variables, and in-hospital death was used as the dependent variable. Binary logistic regression analysis modeling was performed, and risk factor prompts corresponding to specific indicators were given according to the big data training model, and a forest map was drawn. Then, the trained model was input with the data of the patient's personal chronic disease map, and the comprehensive evaluation results of the patient's risk factors were output.

[0045] Example 2 (neural network machine learning): The patient node and the chronic disease node (including diagnostic test information data and medication information data) of the patient's personal chronic disease map are input into the fully connected layer network to obtain updated embedding vector representations of the patient node and the chronic disease (chronic ischemic heart disease, hypertension, diabetes and cerebrovascular disease) nodes; the updated embedding vector representations of the patient node and the chronic disease node are input into the decoding prediction module to obtain the prediction results of chronic disease comorbidity risk factors, wherein the decoding prediction module includes a bilinear decoder, which is used to decode the embedding vector representations of the patient node and the chronic disease node, and evaluate the probability of occurrence of patient risk factors based on the decoding results.

[0046] The validation of risk factor prediction models is mainly divided into internal validation and external validation. The commonly used internal validation in this field is to use the data of the modeling set for validation, including random splitting, cross validation and Bootstrap sampling. The random splitting method is to randomly divide the data set into a training set and a validation set according to a certain ratio, build a prediction model with the training set data, and verify the prediction performance of the model with the validation set data. Since the two data sets are very similar, more ideal results are often obtained. Compared with the random sampling method, when the data is limited, the cross validation method and the bootstrap sampling method are more desirable, which can reduce the similarity between the training set and the validation set. For the two methods mentioned above, the modeling set can be used for internal validation by resampling 500 times through the Bootstrap method; 300 validation set data are used for external validation to confirm that the accuracy of risk occurrence judgment is high.

[0047] The commonly used external validation in this field is to evaluate the model using data different from the modeling set. According to the data source, it can be divided into time validation, regional validation and strong external validation. Time validation is to evaluate the subsequent prediction effect of the risk prediction model on the modeling object; regional validation is to validate with other research subjects from the same region as the modeling object; strong external validation is to evaluate with research subjects from other research centers that are completely different from the modeling set. Strong external validation is considered to be the most ideal external validation method. The evaluation indicators of the risk factor prediction model are mainly discrimination and calibration. Discrimination refers to the ability of the model to predict the correctness of the occurrence of an event. It is usually evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). When AUC is 0.5-0.7, it indicates a low predictive value, AUC of 0.7-0.9 indicates a medium predictive value, and AUC above 0.9 indicates a high predictive value. The closer the value is to 1.0, the better the discrimination of the model. The degree of calibration reflects the degree to which the model correctly estimates the absolute risk. It is usually evaluated by the HL goodness-of-fit test, which mainly compares whether the difference between the expected probability and the actual probability of an event is statistically significant. P>0.05 indicates that the model has a good goodness-of-fit effect.

[0048] The above-mentioned embodiments are only preferred implementations of the present invention, and are only used to help understand the method and core ideas of the present application. The protection scope of the present invention is not limited to the above-mentioned embodiments. All technical solutions under the idea of ​​the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. An intelligent management system for chronic comorbidities in the elderly, characterized by: include: Comprehensive geriatric assessment module, multidisciplinary collaborative diagnosis and treatment module, and multiple medication risk prediction and management module; The comprehensive assessment module for the elderly constructs a personal chronic disease map for the elderly based on their personal information, diagnostic data, and medication information. The map is then input into the risk factor prediction model for prediction, and the health risk warning and comprehensive assessment results for the patient are output. The multidisciplinary collaborative diagnosis and treatment module is used by the first-visit / receiving doctor to coordinate multidisciplinary doctors to conduct collaborative diagnosis and treatment based on the comprehensive assessment results of the geriatric comprehensive assessment module; The multiple medication risk prediction management module is used for the first-visit / admitting physician to provide comprehensive diagnosis, multiple medication management and risk prediction to the patient through the collaborative diagnosis and treatment.

2. The intelligent management system for chronic comorbidities in the elderly according to claim 1 is characterized in that: The comprehensive elderly assessment module sets up a personal information database to store the patient's personal information, sets up a diagnostic test database to store the patient's diagnostic data of tests and all related examinations during the patient's visit and hospitalization, and sets up a medication information database to store the patient's daily and hospitalized medication information.

3. The intelligent management system for chronic comorbidities in the elderly according to claim 1 is characterized in that: The comprehensive elderly assessment module also includes: obtaining a scale score according to the patient's evaluation scale, inputting the patient's personal chronic disease map and scale score into a risk factor prediction model for prediction, and outputting health risk prompts and comprehensive assessment results for the patient.

4. The intelligent management system for chronic comorbidities in the elderly according to claim 3 is characterized in that: The comprehensive geriatric assessment module is provided with an assessment scale database to store assessment scales covering physical function, geriatric syndrome, mental and psychological conditions, and the Charlson comorbidity index.

5. The intelligent management system for chronic comorbidities in the elderly according to claim 1 is characterized in that: The risk factor prediction model is one or more of a multi-factor logistic regression model, a Cox regression model, a neural network machine learning model, and a time series disease network model.

6. The intelligent management system for chronic comorbidities in the elderly according to claim 1 is characterized in that: The multidisciplinary collaborative diagnosis and treatment module includes a multidisciplinary integrated triage platform and a multidisciplinary patient precision clinic; the multidisciplinary integrated triage platform includes the doctor information of each department, and the first-visit / admitting doctor comprehensively selects the required consultation departments and relevant doctors based on the comprehensive evaluation results, and the above-mentioned relevant doctors jointly form a multidisciplinary patient precision clinic; the multidisciplinary patient precision clinic is a specialty related to the diagnosis and treatment of chronic diseases, including geriatrics, endocrinology and metabolism, cardiovascular medicine, respiratory medicine, gastroenterology, nephrology, neurology, neurosurgery, oncology, clinical psychology, clinical nutrition, rehabilitation medicine, pharmacy, and nursing department.

7. The intelligent management system for chronic comorbidities in the elderly according to claim 1 is characterized in that: The multiple medication risk prediction management module includes comprehensive diagnostic testing, multiple medication management, and diet and daily life management functions.

8. The intelligent management system for chronic comorbidities in the elderly according to claim 7 is characterized in that: Multiple medication management follows the principles of non-drug treatment priority, benefit principle, five-drug principle, small dose principle, timing principle and medication suspension principle; the multiple medication management also includes AI medication warning to promptly alert patients of medication contraindications.

9. The intelligent management system for chronic comorbidities in the elderly according to claim 1 is characterized in that: It also includes a chronic disease intelligent follow-up management module, which includes a follow-up tracking function, a reexamination reminder function and a risk factor reminder function, and performs follow-up tracking, health risk prediction and reexamination reminders for elderly patients with chronic diseases and comorbidities.

10. The intelligent management system for chronic comorbidities in the elderly according to claim 1 is characterized in that: It also includes a health science knowledge base module, which includes a medical health education science knowledge base. The medical health education science knowledge base is dynamically updated, and according to the diagnosis results made by the multidisciplinary patient precision clinic, the corresponding health education knowledge is timely matched and pushed to the patient.

Images