Chronic disease management system for hospital

Abstract

The invention discloses a chronic disease management system for hospitals, which relates to the technical field of chronic disease management systems and comprises a patient file module, a risk assessment module, an intervention scheme module, a follow-up management module, a monitoring and early warning module and a decision support module. The patient archive module is used for collecting and sorting personal information and medical history information of patients by adopting an automatic data integration method to obtain a complete patient health archive; the risk assessment module is used for analyzing the health information in the patient health record by adopting an intelligent assessment algorithm to obtain a chronic disease risk assessment result; the intervention scheme module is used for formulating a personalized health intervention scheme according to the risk assessment result, and the personalized health intervention scheme comprises diet, exercise and medication suggestions; and the follow-up management module is used for executing the follow-up plan in the intervention scheme through multiple communication modes, tracking the rehabilitation condition of the patient, recording and feeding back, and generating a follow-up report.

Description

Technical Field

[0001] The present invention relates to the technical field of chronic disease management systems, and in particular to a chronic disease management system for hospitals. Background Art

[0002] Chronic disease management systems are holistic solutions that leverage information technology to help medical institutions more effectively manage patients with chronic diseases. Therefore, leveraging advanced technologies to enhance the intelligence and security of chronic disease management systems has become a pressing issue.

[0003] In the field of chronic disease management systems, in traditional chronic disease management systems, patients' personal information, medical history information, test results, etc. are usually scattered in different medical systems or paper records, making it difficult for doctors to obtain comprehensive patient health records, affecting the quality and efficiency of diagnosis and treatment decisions. In addition, most current health management plans adopt universal diet, exercise and medication recommendations, ignoring individual differences. This not only reduces the treatment effect, but also causes adverse reactions because it is not suitable for the actual situation of specific patients. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a chronic disease management system for hospitals to solve the problem that in the traditional chronic disease management system, patients' personal information, medical history information, examination results, etc. are usually scattered in different medical systems or paper records, making it difficult for doctors to obtain comprehensive patient health records, affecting the quality and efficiency of diagnosis and treatment decisions.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a chronic disease management system for a hospital, comprising:

[0008] Patient file module, risk assessment module, intervention plan module, follow-up management module, monitoring and early warning module and decision support module;

[0009] The patient file module is used to collect and organize the patient's personal information and medical history information using an automated data integration method to obtain a complete patient health file;

[0010] The risk assessment module is used to analyze the health information in the patient's health record using an intelligent assessment algorithm to obtain a chronic disease risk assessment result;

[0011] The intervention plan module is used to formulate a personalized health intervention plan based on the risk assessment results, including diet, exercise and medication recommendations;

[0012] The follow-up management module is used to execute the follow-up plan in the intervention program through multiple communication methods, track the patient's recovery status and record feedback, and generate a follow-up report;

[0013] The monitoring and early warning module is used to monitor the patient's physical indicators in real time using wearable devices, compare them with the preset safety range, and issue early warning notifications when abnormalities are found;

[0014] The decision support module is used to use data analysis technology to perform statistical analysis on the data collected by the follow-up management module and the monitoring and early warning module, and provide treatment adjustment suggestions and support to doctors.

[0015] As a preferred solution of the hospital chronic disease management system of the present invention, the automated data integration method is used to collect and organize the patient's personal information and medical history information to obtain a complete patient health record. The specific steps are as follows:

[0016] Use API interface to automatically obtain original data of patients' personal information and medical history information from hospital information system;

[0017] Use the standardization function to clean and standardize the extracted data;

[0018] The standardized data are merged through the fusion function to generate the final patient health record.

[0019] As a preferred solution of the hospital chronic disease management system of the present invention, wherein: the intelligent assessment algorithm is used to analyze the health information in the patient health record to obtain the chronic disease risk assessment result, and the specific steps are:

[0020] The standardized patient health records are further cleaned using data preprocessing functions;

[0021] Use the health scoring function to perform a preliminary score on the cleaned data and obtain the preliminary score result H(P(z)), which is expressed as:

[0022]

[0023] Among them, w i is the weight coefficient, c i is the ideal health standard value, m is the number of health indicators, and P(z) is the cleaned data;

[0024] The risk prediction model is used to conduct an in-depth analysis of the preliminary scoring result H(P(z)) to obtain the final risk assessment result R, which is expressed as:

[0025]

[0026] Where h(t) is the time-dependent risk function and t is the time variable.

[0027] As a preferred solution of the hospital chronic disease management system of the present invention, the steps of formulating a personalized health intervention plan based on the risk assessment results, including diet, exercise and medication recommendations, are as follows:

[0028] Use personalized intervention models to analyze chronic disease risk assessment results;

[0029] Use a dietary recommendation algorithm to generate personalized dietary recommendations based on the analyzed intervention needs I(R);

[0030] An exercise recommendation algorithm is used to generate personalized exercise recommendations based on the analyzed intervention requirements I(R) to obtain exercise recommendations;

[0031] Using the medication recommendation algorithm, a personalized medication recommendation is generated based on the parsed intervention requirement I(R), which is expressed as:

[0032]

[0033] Among them, m n is the efficacy score of each drug, I n is the corresponding intervention need score, and r is the number of drug types.

[0034] As a preferred solution of the hospital chronic disease management system of the present invention, wherein: the follow-up plan in the intervention plan is executed through multiple communication methods, the patient's recovery status is tracked and feedback is recorded, and a follow-up report is generated. The specific steps are:

[0035] Use a communication interface to extract follow-up plans from personalized health intervention plans;

[0036] Use communication platforms to send follow-up notifications to patients;

[0037] A feedback recording module is used to receive and record the patient's feedback information to obtain a feedback information set;

[0038] The analysis module is used to analyze the feedback information and generate a follow-up report. The follow-up report is expressed as follows:

[0039]

[0040] Among them, a(f) is the function for analyzing the i-th feedback information.

[0041] As a preferred solution of the hospital chronic disease management system of the present invention, wherein: the wearable device is used to monitor the patient's physical indicators in real time, and compares them with the preset safety range, and issues an early warning notification when an abnormality is found. The specific steps are as follows:

[0042] A data acquisition module is used to obtain the patient's real-time physical indicators from the wearable device to obtain a physical indicator set;

[0043] Use the safety range comparison function to compare the real-time data with the preset safety range to obtain the comparison result;

[0044] Detect abnormal situations and generate abnormal reports, and issue early warning notifications based on abnormal reports. The expression is:

[0045]

[0046] Among them, e(a i ) is the notification function that sends the i-th exception report.

[0047] As a preferred solution of the hospital chronic disease management system of the present invention, the data analysis technology is used to statistically analyze the data collected by the follow-up management module and the monitoring and early warning module to provide doctors with treatment adjustment suggestions and support. The specific steps are as follows:

[0048] The data integration module is used to extract data from the follow-up management module and the monitoring and early warning module to obtain a data item set;

[0049] Statistical analysis algorithms were used to conduct preliminary analysis on the extracted data and obtain preliminary analysis results;

[0050] Apply machine learning models to conduct in-depth analysis of the preliminary analysis results and obtain in-depth analysis results;

[0051] Generate treatment adjustment suggestions based on the in-depth analysis results to obtain a treatment adjustment suggestion set;

[0052] Send treatment adjustment suggestions to the doctor and get the results.

[0053] As a preferred solution of the hospital chronic disease management system of the present invention, the application of the machine learning model to perform in-depth analysis on the preliminary analysis results and obtain in-depth analysis results is specifically carried out in the following steps:

[0054] Using a machine learning model input interface to receive preliminary analysis results output from the statistical analysis algorithm, the results are derived from the data integration and analysis process of the follow-up management module and the monitoring and early warning module;

[0055] The first type of time series data is modeled using a linear regression function to obtain a linear trend component;

[0056] The logistic regression function is used to model the categorical time series and obtain the probability of event occurrence;

[0057] The high volatility index is smoothed using a logarithmic nonlinear function to obtain a nonlinear correction component;

[0058] The three types of characteristic functions are weighted and then time-integrated to obtain the depth analysis results.

[0059] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the hospital chronic disease management system as described in the first aspect of the present invention is implemented.

[0060] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the hospital chronic disease management system as described in the first aspect of the present invention is implemented.

[0061] The beneficial effects of the present invention are as follows: by merging the standardized data through a fusion function to generate the final patient health record, by using the fusion function to perform weighted integration of multi-source standardized data, a structured electronic record that comprehensively reflects the patient's health status is formed, and a unified data view is provided for subsequent risk assessment and intervention decisions, thereby achieving the effect of enhancing the availability of records and supporting personalized medical services. By introducing weight coefficients and ideal health standard values ​​to construct a scoring function, a quantitative scoring of the patient's health status is achieved, providing a numerical basis for subsequent risk prediction, and by dynamically modeling the scoring results based on a time-dependent risk function, a predictive evaluation of the development trend of chronic diseases is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0063] Figure 1 This is a schematic diagram of the chronic disease management system for hospitals in Example 1. DETAILED DESCRIPTION

[0064] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0066] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0067] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides a chronic disease management system for a hospital, comprising:

[0068] Patient file module, risk assessment module, intervention plan module, follow-up management module, monitoring and early warning module and decision support module;

[0069] The patient file module is used to collect and organize the patient's personal information and medical history information using automated data integration methods to obtain a complete patient health file;

[0070] Furthermore, the API interface is used to automatically obtain the original data of the patient's personal information and medical history information from the hospital information system;

[0071] Use the standardization function to clean and standardize the extracted data;

[0072] The standardized data are merged through the fusion function to generate the final patient health record;

[0073] It should be noted that the API interface is compatible with multiple hospital information system protocols, supports structured and unstructured data access, and ensures comprehensive patient information collection and real-time updates; the standardization function adopts a unified data dictionary and unit conversion mechanism to eliminate differences in data formats from different sources; the fusion function integrates multi-source data based on a weight distribution strategy to generate a unified and complete patient health record, providing a high-quality data foundation for subsequent analysis.

[0074] The risk assessment module is used to analyze the health information in the patient's health record using an intelligent assessment algorithm to obtain chronic disease risk assessment results;

[0075] Furthermore, the standardized patient health records are further cleaned using data preprocessing functions;

[0076] Use the health scoring function to perform a preliminary score on the cleaned data and obtain the preliminary score result H(P(z)), which is expressed as:

[0077]

[0078] Among them, w i is the weight coefficient, c i is the ideal health standard value, m is the number of health indicators, and P(z) is the cleaned data;

[0079] The risk prediction model is used to conduct an in-depth analysis of the preliminary scoring result H(P(z)) to obtain the final risk assessment result R, which is expressed as:

[0080]

[0081] Where h(t) is the time-dependent risk function and t is the time variable;

[0082] It should be noted that the data preprocessing function includes operations such as missing value filling, outlier removal and data normalization to improve the quality of input data; the health scoring function realizes the quantitative expression of health status by setting ideal health standard values ​​and weighted calculation of individual indicator deviations; the risk prediction model introduces time dimension modeling, combines historical data to dynamically evaluate the risk of chronic disease development, and improves prediction accuracy and clinical applicability.

[0083] The intervention plan module is used to develop personalized health intervention plans based on risk assessment results, including diet, exercise and medication recommendations;

[0084] Furthermore, a personalized intervention model is used to analyze the results of chronic disease risk assessment;

[0085] Use a dietary recommendation algorithm to generate personalized dietary recommendations based on the analyzed intervention needs I(R);

[0086] An exercise recommendation algorithm is used to generate personalized exercise recommendations based on the analyzed intervention requirements I(R) to obtain exercise recommendations;

[0087] Using the medication recommendation algorithm, a personalized medication recommendation is generated based on the parsed intervention requirement I(R), which is expressed as:

[0088]

[0089] Among them, m n is the efficacy score of each drug, I n is the corresponding intervention need score, r is the number of drug types;

[0090] It should be noted that the personalized intervention model generates differentiated intervention paths based on the patient's risk level, disease type, physiological characteristics and other factors; the diet recommendation algorithm combines the nutritional database with individual metabolic characteristics to match food; the exercise recommendation algorithm considers the patient's physical fitness level and living habits to formulate a safe and effective exercise plan; the medication recommendation algorithm integrates the drug efficacy score with the current disease needs to assist doctors in formulating scientific medication plans.

[0091] Follow-up management module, which is used to implement the follow-up plan in the intervention program through various communication methods, track the patient's recovery status and record feedback, and generate follow-up reports;

[0092] Furthermore, a communication interface is used to extract follow-up plans from personalized health intervention plans;

[0093] Use communication platforms to send follow-up notifications to patients;

[0094] A feedback recording module is used to receive and record the patient's feedback information to obtain a feedback information set;

[0095] The analysis module is used to analyze the feedback information and generate a follow-up report. The follow-up report is expressed as follows:

[0096]

[0097] Among them, a(f i ) is the function for analyzing the i-th feedback information;

[0098] It should be noted that the communication interface can adapt to various notification methods such as SMS, WeChat, and telephone to ensure that the follow-up plan is effectively conveyed to the patient; the feedback record module has automatic identification and classification functions, which can extract and store key rehabilitation information; the follow-up report generation function is based on feedback frequency, content completeness and health change trends. Intelligent summary forms structured follow-up results to facilitate doctors to quickly grasp the patient's recovery status.

[0099] The monitoring and early warning module is used to monitor the patient's physical indicators in real time using wearable devices, compare them with the preset safety range, and issue early warning notifications when abnormalities are found;

[0100] Furthermore, a data acquisition module is used to obtain the patient's real-time physical indicators from the wearable device to obtain a physical indicator set;

[0101] Use the safety range comparison function to compare the real-time data with the preset safety range to obtain the comparison result;

[0102] Detect abnormal situations and generate abnormal reports, and issue early warning notifications based on abnormal reports. The expression is:

[0103]

[0104] Among them, e(a i ) is the notification function for sending the i-th exception report;

[0105] It should be noted that wearable devices include but are not limited to smart bracelets, electrocardiographs, blood glucose meters and other terminal devices with real-time monitoring capabilities; the safety range comparison function sets individual thresholds based on medical guidelines to avoid false alarms and missed alarms caused by universal standards; the early warning notification mechanism supports graded responses, prompting patients to pay attention to mild abnormalities, and immediately pushing severe abnormalities to the doctor-side system to ensure timely intervention.

[0106] The decision support module uses data analysis technology to perform statistical analysis on the data collected by the follow-up management module and the monitoring and early warning module, and provides doctors with treatment adjustment suggestions and support;

[0107] Furthermore, a data integration module is used to extract data from the follow-up management module and the monitoring and early warning module to obtain a set of data items;

[0108] Statistical analysis algorithms were used to conduct preliminary analysis on the extracted data and obtain preliminary analysis results;

[0109] Apply machine learning models to conduct in-depth analysis of the preliminary analysis results and obtain in-depth analysis results;

[0110] The machine learning model input interface is used to receive the preliminary analysis results output from the statistical analysis algorithm. The results come from the data integration and analysis process of the follow-up management module and the monitoring and early warning module;

[0111] The first type of time series data is modeled using a linear regression function to obtain a linear trend component;

[0112] The logistic regression function is used to model the categorical time series and obtain the probability of event occurrence;

[0113] The high volatility index is smoothed using a logarithmic nonlinear function to obtain a nonlinear correction component;

[0114] After weighting the three types of characteristic functions, time integration operation is performed to obtain the depth analysis results;

[0115] Generate treatment adjustment suggestions based on the in-depth analysis results to obtain a treatment adjustment suggestion set;

[0116] Send treatment adjustment suggestions to doctors and obtain sending results;

[0117] It should be noted that the data integration module has the ability to clean and label multi-source heterogeneous data, providing a unified data view for statistical analysis; the statistical analysis algorithm covers descriptive statistics, correlation analysis and other methods to explore the potential patterns of the data; the machine learning model integrates linear, logical and nonlinear functions, and combines the time integration mechanism to model long-term trends, output treatment adjustment suggestions, and improve the efficiency and scientificity of diagnosis and treatment.

[0118] This embodiment also provides a computer device suitable for the chronic disease management system used in hospitals, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the chronic disease management system used in hospitals proposed in the above embodiment.

[0119] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0120] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the hospital chronic disease management system proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0121] In summary, the present invention generates a final patient health record by merging the standardized data through a fusion function, and realizes the formation of a structured electronic record that comprehensively reflects the patient's health status by using a fusion function to perform weighted integration of multi-source standardized data, thereby providing a unified data view for subsequent risk assessment and intervention decisions, thereby achieving the effect of enhancing the availability of records and supporting personalized medical services, and constructing a scoring function by introducing weight coefficients and ideal health standard values ​​to achieve quantitative scoring of the patient's health status, providing a numerical basis for subsequent risk prediction, and dynamically modeling the scoring results based on a time-dependent risk function to achieve a predictive evaluation of the development trend of chronic diseases.

[0122] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A chronic disease management system for hospitals, characterized by: include: Patient file module, risk assessment module, intervention plan module, follow-up management module, monitoring and early warning module and decision support module; The patient file module is used to collect and organize the patient's personal information and medical history information using an automated data integration method to obtain a complete patient health file; The risk assessment module is used to analyze the health information in the patient's health record using an intelligent assessment algorithm to obtain a chronic disease risk assessment result; The intervention plan module is used to formulate a personalized health intervention plan based on the risk assessment results, including diet, exercise and medication recommendations; The follow-up management module is used to execute the follow-up plan in the intervention program through multiple communication methods, track the patient's recovery status and record feedback, and generate a follow-up report; The monitoring and early warning module is used to monitor the patient's physical indicators in real time using wearable devices, compare them with the preset safety range, and issue early warning notifications when abnormalities are found; The decision support module is used to use data analysis technology to perform statistical analysis on the data collected by the follow-up management module and the monitoring and early warning module, and provide treatment adjustment suggestions and support to doctors.

2. The chronic disease management system for hospitals according to claim 1, characterized in that: The automated data integration method is used to collect and organize the patient's personal information and medical history information to obtain a complete patient health record. The specific steps are as follows: Use API interface to automatically obtain original data of patients' personal information and medical history information from hospital information system; Use the standardization function to clean and standardize the extracted data; The standardized data are merged through the fusion function to generate the final patient health record.

3. The chronic disease management system for hospitals according to claim 2, characterized in that: The intelligent assessment algorithm is used to analyze the health information in the patient's health record to obtain a chronic disease risk assessment result. The specific steps are: The standardized patient health records are further cleaned using data preprocessing functions; Use the health scoring function to perform a preliminary score on the cleaned data and obtain the preliminary score result H(P(z)), which is expressed as: Among them, w i is the weight coefficient, c i is the ideal health standard value, m is the number of health indicators, and P(z) is the cleaned data; The risk prediction model is used to conduct an in-depth analysis of the preliminary scoring result H(P(z)) to obtain the final risk assessment result R, which is expressed as: Where h(t) is the time-dependent risk function and t is the time variable.

4. The hospital chronic disease management system according to claim 3, characterized in that: The personalized health intervention plan is formulated based on the risk assessment results, including diet, exercise and medication recommendations. The specific steps are as follows: Use personalized intervention models to analyze chronic disease risk assessment results; Use a dietary recommendation algorithm to generate personalized dietary recommendations based on the analyzed intervention needs I(R); An exercise recommendation algorithm is used to generate personalized exercise recommendations based on the analyzed intervention requirements I(R) to obtain exercise recommendations; Using the medication recommendation algorithm, a personalized medication recommendation is generated based on the parsed intervention requirement I(R), which is expressed as: Among them, m n is the efficacy score of each drug, I n is the corresponding intervention need score, and r is the number of drug types.

5. The chronic disease management system for hospitals according to claim 4, characterized in that: The specific steps of executing the follow-up plan in the intervention program through multiple communication methods, tracking the patient's recovery status and recording feedback, and generating a follow-up report are as follows: Use a communication interface to extract follow-up plans from personalized health intervention plans; Use communication platforms to send follow-up notifications to patients; A feedback recording module is used to receive and record the patient's feedback information to obtain a feedback information set; The analysis module is used to analyze the feedback information and generate a follow-up report. The follow-up report is expressed as follows: Among them, a(f i ) is the function for analyzing the i-th feedback information.

6. The chronic disease management system for hospitals according to claim 5, characterized in that: The wearable device is used to monitor the patient's physical indicators in real time, and compares them with the preset safety range. When an abnormality is found, an early warning notification is issued. The specific steps are as follows: A data acquisition module is used to obtain the patient's real-time physical indicators from the wearable device to obtain a physical indicator set; Use the safety range comparison function to compare the real-time data with the preset safety range to obtain the comparison result; Detect abnormal situations and generate abnormal reports, and issue early warning notifications based on abnormal reports. The expression is: Among them, e(a i ) is the notification function that sends the i-th exception report.

7. The chronic disease management system for hospitals according to claim 6, characterized in that: The data analysis technology is used to perform statistical analysis on the data collected by the follow-up management module and the monitoring and early warning module to provide doctors with treatment adjustment suggestions and support. The specific steps are as follows: The data integration module is used to extract data from the follow-up management module and the monitoring and early warning module to obtain a data item set; Statistical analysis algorithms were used to conduct preliminary analysis on the extracted data and obtain preliminary analysis results; Apply machine learning models to conduct in-depth analysis of the preliminary analysis results and obtain in-depth analysis results; Generate treatment adjustment suggestions based on the in-depth analysis results to obtain a treatment adjustment suggestion set; Send treatment adjustment suggestions to the doctor and get the results.

8. The chronic disease management system for hospitals according to claim 7, characterized in that: The application of the machine learning model to conduct an in-depth analysis of the preliminary analysis results and obtain in-depth analysis results, specifically involves the following steps: Using a machine learning model input interface to receive preliminary analysis results output from the statistical analysis algorithm, the results are derived from the data integration and analysis process of the follow-up management module and the monitoring and early warning module; The first type of time series data is modeled using a linear regression function to obtain a linear trend component; The logistic regression function is used to model the categorical time series and obtain the probability of event occurrence; The high volatility index is smoothed using a logarithmic nonlinear function to obtain a nonlinear correction component; The three types of characteristic functions are weighted and then time-integrated to obtain the depth analysis results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the hospital chronic disease management system according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the hospital chronic disease management system according to any one of claims 1 to 8 are implemented.

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