Liver care system and whole-process management and control method for chronic disease prevention and control

Abstract

The application discloses a liver care system and a whole-process management method for chronic disease prevention and control. The system comprises a perception layer, a data layer, an application layer and a display layer connected in sequence. The perception layer collects multi-dimensional health data. The data layer constructs a liver disease specialist data resource pool. The application layer realizes four core functions. The display layer supports multi-terminal visual access. The management method comprises four closed-loop stages of precise screening, diagnosis and treatment intervention, out-of-hospital management and dynamic optimization. Through hierarchical detection and artificial intelligence assisted decision-making, an individualized scheme is generated. Combined with the doctor-patient collaborative interaction mechanism, whole-process monitoring and dynamic adjustment of the scheme are realized. A simple formula is introduced to quantify the risk and intervention effect. The scheme breaks the barriers of in-hospital and out-of-hospital management, improves the precision of chronic liver disease prevention and control and the patient compliance, guarantees data security, provides support for clinical research and policy making, and is suitable for multi-scene popularization.

Description

Technical Field

[0001] This invention relates to the field of liver care, specifically to a liver care system and comprehensive management method for chronic disease prevention and control. Background Technology

[0002] Chronic liver disease, as a prevalent chronic illness, progresses slowly and often presents with subtle early symptoms. It is prone to developing into serious complications such as liver fibrosis and liver cancer, placing a heavy burden on patients' health and the healthcare system. Currently, liver care faces numerous challenges in the context of chronic disease prevention and control: Traditional management models suffer from a severe disconnect between in-hospital treatment and out-of-hospital monitoring, making it difficult to provide doctors with real-time feedback on patients' out-of-hospital health data, leading to delays in intervention adjustments; screening processes lack tiered design, relying heavily on invasive liver biopsies to assess liver fibrosis, which has low patient acceptance and is unsuitable for large-scale implementation at the grassroots level; data is scattered across different healthcare systems, creating information silos and failing to provide comprehensive data support for accurate assessment and decision-making; limited doctor-patient interaction channels result in poor patient follow-up adherence and inadequate implementation of lifestyle interventions, impacting prevention and control effectiveness. Furthermore, existing nursing systems often focus on single treatment stages, lacking a closed-loop management system encompassing screening, intervention, monitoring, and optimization, making it difficult to meet the refined and personalized needs of chronic disease prevention and control. Therefore, there is an urgent need to build a liver care system and method that integrates multi-source data and achieves full-process collaborative management to solve the shortcomings of the existing management model and improve the accuracy and efficiency of chronic liver disease prevention and control. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to overcome the defects of the above-mentioned technologies and provide a liver care system and a whole-process management method for chronic disease prevention and control.

[0004] To address the aforementioned technical problems, the present invention provides a liver care system and a comprehensive management method for chronic disease prevention and control: a liver care system for chronic disease prevention and control.

[0005] The system comprises a perception layer, a data layer, an application layer, and a presentation layer, which are sequentially and collaboratively connected. The perception layer is used to comprehensively collect patients' in-hospital treatment data, home monitoring data, and health record data to form a multi-dimensional health data set. The data layer is used to standardize, clean, process for privacy and security, and store the multi-dimensional health data set in a structured manner to build a unified liver disease specialty data resource pool. Based on the liver disease specialty data resource pool, the application layer realizes four core functions: risk assessment, personalized intervention plan generation, full-cycle follow-up management, and targeted patient education. The presentation layer adapts to the access needs of different users, supports multi-terminal login, and presents the assessment results, intervention plans, and health data change trends output by the application layer in a visual form, providing data support for doctor-patient interaction.

[0006] As an improvement, the perception layer includes in-hospital medical equipment, home IoT devices, and medical data sources. The in-hospital medical equipment includes transient elastography equipment and liver function testing instruments. The home IoT devices include smart bracelets and blood glucose and lipid monitors. The medical data sources include electronic medical records and hospital information systems.

[0007] As an improvement, the data layer constructs a liver disease specialty data model based on the health information exchange standard. The data layer is configured with a data cleaning module, a dynamic desensitization middleware, and a blockchain traceability module. The blockchain traceability module is used to realize full-process traceability of data access.

[0008] As an improvement, the risk assessment module of the application layer integrates a scoring system and a deep learning model. The deep learning model is used to integrate liver function data, liver stiffness data, and metabolic index data to achieve liver cancer risk prediction and liver fibrosis staging assessment.

[0009] A comprehensive management approach for liver care systems aimed at chronic disease prevention and control includes four sequentially linked and closed-loop stages: precise screening, diagnosis and intervention, outpatient management, and dynamic optimization. The precise screening stage involves stratified testing of the general population and high-risk individuals to determine and stratify the risk level of chronic liver disease. The diagnosis and intervention stage, based on the risk stratification results from the precise screening stage and combined with the patient's individual physiological characteristics, complications, and medical history, generates a suitable individualized treatment plan and lifestyle intervention program. The outpatient management stage utilizes remote data collection, real-time indicator monitoring, and interactive health guidance to continuously track the patient's outpatient health status and provide early warnings of abnormal conditions. The dynamic optimization stage, based on the implementation effects of the diagnosis and intervention stage and the monitoring data from the outpatient management stage, dynamically adjusts the treatment plan and intervention program, forming a closed-loop management system encompassing screening, intervention, monitoring, and optimization.

[0010] As an improvement, the precision screening stage follows the principle of moving from basic to in-depth and from non-invasive to invasive. Primary care institutions complete the initial screening through liver function tests and abdominal ultrasound. For high-risk groups, transient elastography equipment is used to conduct precise assessment of liver fibrosis. The liver care system for chronic disease prevention and control automatically integrates the screening data and generates risk stratification reports of low risk, medium risk, or high risk.

[0011] As an improvement, in the diagnosis and treatment intervention stage, doctors obtain patients' dynamic health records through the liver care system for chronic disease prevention and control. The artificial intelligence-assisted decision-making module of the application layer recommends individualized treatment plans, including drug optimization plans and surgical suggestions, based on clinical guidelines and multi-omics data, and generates lifestyle intervention plans related to dietary restrictions, smoking cessation and alcohol limitation.

[0012] As an improvement, during the outpatient management phase, patients upload home monitoring data through the mobile terminal of the display layer. The liver care system for chronic disease prevention and control tracks changes in liver function and liver stiffness data in real time. If an abnormality occurs, it automatically triggers a doctor's alarm. The AI ​​health manager in the application layer pushes personalized science popularization content and intervention reminders.

[0013] As an improvement, a doctor-patient collaborative interaction mechanism is set up in the precise screening stage, the diagnosis and treatment intervention stage, the outpatient management stage, and the dynamic optimization stage. The doctor-patient collaborative interaction mechanism includes doctors pushing diagnosis and treatment suggestions, intervention requirements, and health inquiries to patients through the PC terminal of the display layer, and patients providing feedback on the implementation status, health needs, and abnormal symptoms through the mobile terminal of the display layer, forming a two-way real-time interaction to ensure the pertinence and efficiency of the management and control process.

[0014] As an improvement, the dynamic optimization phase updates the intervention plan at preset intervals based on the patient's treatment effect, indicator changes, and lifestyle adjustments. The liver care system for chronic disease prevention and control uses big data analysis to uncover disease development patterns and provide data support for clinical research and prevention and control policy formulation.

[0015] The advantages of this invention compared to existing technologies are as follows: This liver care system and comprehensive management method for chronic disease prevention and control breaks down barriers between in-hospital and out-of-hospital management and improves the accuracy of chronic liver disease prevention and control through a collaborative architecture of "perception layer - data layer - application layer - presentation layer" and a closed-loop process of "screening - intervention - monitoring - optimization". The system integrates multi-dimensional health data to construct a specialized liver disease data resource pool, and combines risk scoring and intervention effect evaluation formulas to achieve quantitative decision-making for risk stratification and program optimization, reducing subjective errors. The application of non-invasive testing equipment reduces patient trauma and medical costs, making it suitable for grassroots promotion. Multi-terminal collaboration and AI-powered health management strengthen out-of-hospital management, improving patient follow-up compliance and intervention execution efficiency through a doctor-patient collaborative interaction mechanism. The privacy protection and traceability functions of the data layer ensure data security and standardization, and the accumulated massive amounts of data provide support for clinical research and prevention and control policy formulation. The solution is adaptable to multiple scenarios including hospitals, communities, and families, taking into account the needs of different populations, optimizing the doctor-patient interaction experience, improving the quality of medical services, promoting the rational allocation of medical resources, and effectively reducing the risk of disease progression. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the liver care system for chronic disease prevention and control, and the whole-process management method of the present invention.

[0017] Figure 2 This is a schematic diagram of the liver care system and its full-process management method for chronic disease prevention and control, as described in this invention. Detailed Implementation

[0018] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0020] It is understood that spatial relation terms such as "below," "under," "below," "below," "above," "over," etc., can be used here to describe the relationship between one element or feature shown in the figure and other elements or features. It should be understood that, in addition to the orientation shown in the figure, spatial relation terms also include different orientations of the device in use and operation. For example, if the device in the figure is flipped, the element or feature described as "below" or "under" or "below" of the other element or feature will be oriented "over" the other element or feature. Therefore, the exemplary terms "below" and "under" can include both upper and lower orientations. Furthermore, the device may also include other orientations, such as being rotated 90 degrees or other orientations, and the spatial descriptive terms used herein will be interpreted accordingly.

[0021] It should be noted that when one element is considered to be "connected" to another element, it can be directly connected to the other element or connected to the other element through an intermediary element. In the following embodiments, "connection" should be understood as "electrical connection," "communication connection," etc., if the connected circuits, modules, units, etc., have the transmission of electrical signals or data between them.

[0022] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising,” “including,” or “having,” etc., specify the presence of the stated feature, whole, step, operation, component, part, or combination thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof.

[0023] With reference to the attached diagram, this document describes a liver care system and comprehensive management methods for chronic disease prevention and control.

[0024] The system comprises a perception layer, a data layer, an application layer, and a presentation layer, which are sequentially and collaboratively connected. The perception layer is used to comprehensively collect patients' in-hospital treatment data, home monitoring data, and health record data to form a multi-dimensional health data set. The data layer is used to standardize, clean, process for privacy and security, and store the multi-dimensional health data set in a structured manner to build a unified liver disease specialty data resource pool. Based on the liver disease specialty data resource pool, the application layer realizes four core functions: risk assessment, personalized intervention plan generation, full-cycle follow-up management, and targeted patient education. The presentation layer adapts to the access needs of different users, supports multi-terminal login, and presents the assessment results, intervention plans, and health data change trends output by the application layer in a visual form, providing data support for doctor-patient interaction.

[0025] The perception layer includes in-hospital medical equipment, home IoT devices, and medical data sources. The in-hospital medical equipment includes transient elastography equipment and liver function testing instruments. The home IoT devices include smart bracelets and blood glucose and lipid monitors. The medical data sources include electronic medical records and hospital information systems.

[0026] The data layer constructs a liver disease specialty data model based on health information exchange standards. The data layer is configured with a data cleaning module, a dynamic desensitization middleware, and a blockchain traceability module. The blockchain traceability module is used to realize full-process traceability of data access.

[0027] The application layer's risk assessment module integrates a scoring system and a deep learning model. The deep learning model is used to integrate liver function data, liver stiffness data, and metabolic index data to achieve liver cancer risk prediction and liver fibrosis staging assessment.

[0028] A comprehensive management approach for liver care systems aimed at chronic disease prevention and control includes four sequentially linked and closed-loop stages: precise screening, diagnosis and intervention, outpatient management, and dynamic optimization. The precise screening stage involves stratified testing of the general population and high-risk individuals to determine and stratify the risk level of chronic liver disease. The diagnosis and intervention stage, based on the risk stratification results from the precise screening stage and combined with the patient's individual physiological characteristics, complications, and medical history, generates a suitable individualized treatment plan and lifestyle intervention program. The outpatient management stage utilizes remote data collection, real-time indicator monitoring, and interactive health guidance to continuously track the patient's outpatient health status and provide early warnings of abnormal conditions. The dynamic optimization stage, based on the implementation effects of the diagnosis and intervention stage and the monitoring data from the outpatient management stage, dynamically adjusts the treatment plan and intervention program, forming a closed-loop management system encompassing screening, intervention, monitoring, and optimization.

[0029] The precise screening phase follows the principle of moving from basic to in-depth and from non-invasive to invasive. Primary care institutions complete the initial screening through liver function tests and abdominal ultrasound. For high-risk individuals, transient elastography is used to conduct precise assessment of liver fibrosis. The liver care system for chronic disease prevention and control automatically integrates the screening data and generates risk stratification reports of low risk, medium risk, or high risk.

[0030] During the diagnosis and treatment intervention phase, doctors obtain patients' dynamic health records through the liver care system for chronic disease prevention and control. The AI-assisted decision-making module at the application layer recommends individualized treatment plans, including drug optimization and surgical suggestions, based on clinical guidelines and multi-omics data. At the same time, it generates lifestyle intervention plans related to dietary restrictions, smoking cessation, and alcohol limitation.

[0031] During the outpatient management phase, patients upload home monitoring data through the mobile terminal of the display layer. The liver care system for chronic disease prevention and control tracks changes in liver function and liver stiffness data in real time. If an abnormality occurs, it automatically triggers a doctor's alarm. The AI ​​health manager in the application layer pushes personalized science popularization content and intervention reminders.

[0032] A doctor-patient collaborative interaction mechanism is set up in the precise screening stage, the diagnosis and treatment intervention stage, the outpatient management stage, and the dynamic optimization stage. The doctor-patient collaborative interaction mechanism includes doctors pushing diagnosis and treatment suggestions, intervention requirements, and health inquiries to patients through the PC terminal of the display layer, and patients providing feedback on the implementation status, health needs, and abnormal symptoms through the mobile terminal of the display layer, forming a two-way real-time interaction to ensure the pertinence and efficiency of the management and control process.

[0033] The dynamic optimization phase updates the intervention plan at preset intervals based on the patient's treatment effect, indicator changes, and lifestyle adjustments. The liver care system for chronic disease prevention and control uses big data analysis to uncover disease development patterns and provide data support for clinical research and prevention and control policy formulation.

[0034] I. Overall System Deployment and Connection Relationships:

[0035] The liver care system for chronic disease prevention and control disclosed in this embodiment adopts a "cloud-edge-device" collaborative deployment mode, realizing sequential collaborative connection of the perception layer, data layer, application layer and presentation layer. Each layer of devices and modules communicates through encrypted network links to ensure the security and real-time performance of data transmission.

[0036] The in-hospital medical equipment in the perception layer includes transient elastography equipment and liver function testing instruments deployed in hospital departments. The home IoT devices are smart bracelets and blood glucose and lipid monitors distributed to patients. The medical data source is connected to the hospital's electronic medical records and hospital information system through an interface. All of the above devices and data sources upload the collected in-hospital diagnosis and treatment data, home monitoring data and health record data to the data layer in real time in accordance with a unified data transmission protocol.

[0037] The data layer is deployed on a cloud server and uses a liver disease specialty data model built based on health information exchange standards. It predefines field formats and association rules for 12 core data categories, including liver function data, liver stiffness data, and metabolic index data. The data cleaning module removes redundant data and fills in missing values ​​for the uploaded multi-dimensional health data set. A dynamic desensitization middleware replaces and hides private information such as patient names and ID numbers. A blockchain traceability module records the subject, time, and operation content of each data access, ensuring data traceability. The processed data is stored in a distributed database, forming a unified liver disease specialty data resource pool, providing data support for the application layer.

[0038] The application layer runs on cloud computing nodes and implements four core functions based on the liver disease specialist data resource pool. Each functional module works collaboratively through internal interface calls. The presentation layer is adapted to the PC-based doctor workstation used by doctors, as well as the mobile patient application and public account used by patients. It enables multi-terminal access through an encrypted account login mechanism and presents the assessment results, intervention plans, and health data change trends output by the application layer in visual forms such as line charts, pie charts, and text reports.

[0039] II. Specific implementation process of the whole-process control method:

[0040] (I) Implementation of the precise screening phase:

[0041] This phase follows the principle of progressing from basic to in-depth and from non-invasive to invasive methods, conducting stratified testing for the general population and high-risk groups. First, primary healthcare institutions use liver function testing equipment to perform liver function tests on the general population, simultaneously using abdominal ultrasound for liver morphology examination to complete initial screening data collection. For high-risk individuals with a history of chronic hepatitis, a history of long-term alcohol consumption, obesity, or other high-risk factors, transient elastography is used to further assess liver stiffness.

[0042] All screening data is uploaded to the system in real time through the perception layer. The risk assessment module in the application layer initiates the scoring system and deep learning model for analysis. To quantitatively assess the risk level of chronic liver disease, a simple risk scoring formula is introduced:

[0043] in, Represents the risk score for chronic liver disease. These represent standardized values ​​(ranging from 0 to 10) corresponding to various screening data such as liver function indicators, liver stiffness values, and metabolic indicators. These represent the weighting coefficients of each data point (determined based on clinical guidelines and big data analysis, with a total of 1).

[0044] The system uses the calculated risk score Perform risk stratification: when When, it is judged as low risk; when When, it is judged as medium risk; when When a case is identified as high-risk, a corresponding risk stratification report is automatically generated and pushed to the doctor's workstation and the patient's mobile device.

[0045] (II) Implementation of the diagnosis and treatment intervention phase:

[0046] Doctors log in to the system via a PC-based doctor workstation to obtain patients' dynamic health records and risk stratification reports from the precise screening phase. The application-layer AI-assisted decision-making module initiates the individualized treatment plan generation process based on clinical guidelines and multi-omics data.

[0047] First, the module extracts the patient's individual physiological characteristics (such as age, gender, and body mass index), comorbidities (such as whether they have diabetes or hypertension), and medical history. It then combines this information with treatment outcome data for similar patients from the liver disease specialist data resource pool to match and optimize treatment plans. For patients requiring drug therapy, the module recommends conflict-free drug optimization plans based on drug interaction rules. For patients meeting surgical indications, it generates surgical recommendations including suggestions on timing and method of surgery. Simultaneously, it generates lifestyle intervention plans based on the patient's dietary preferences and exercise habits, including dietary restrictions (such as controlling the intake of high-fat and high-sugar foods), smoking cessation and alcohol limitation, and regular exercise.

[0048] Once the generated individualized treatment plan and lifestyle intervention plan have been reviewed and confirmed by the doctor, they are pushed to the patient's mobile device through a presentation layer. The patient can view the details and confirm receipt through the mobile device.

[0049] (III) Implementation of the off-site management phase:

[0050] Patients regularly upload home monitoring data outside the hospital via a mobile application. This includes heart rate and steps collected by a smart bracelet, blood glucose and lipid levels collected by a blood glucose and lipid monitor, and self-recorded diet and medication information. The system tracks changes in liver function and liver stiffness data in real time, sets abnormal warning thresholds, and automatically triggers a doctor's alert when the monitored data exceeds the threshold range, sending a reminder message to the attending physician via the PC-based doctor's workstation.

[0051] The AI-powered health management system at the application layer pushes personalized science content daily based on the patient's risk stratification and intervention plan. For example, it pushes liver care tips to low-risk patients and disease monitoring precautions to high-risk patients. Simultaneously, it generates intervention reminders based on the patient's medication schedule and exercise plan, such as medication reminders and exercise check-in reminders. Patients can provide feedback on the implementation of the intervention plan, health concerns, and abnormal symptoms via mobile devices, while doctors respond with guidance and suggestions via PC, forming a two-way, real-time interactive system.

[0052] (iv) Implementation of the dynamic optimization phase:

[0053] Every three months (preset cycle), the system initiates a dynamic optimization process. The application layer evaluates the original intervention plan based on the implementation effects of the treatment intervention phase (such as symptom improvement and indicator recovery), changes in monitoring data during the outpatient management phase, and patient feedback on lifestyle adjustments.

[0054] Introducing a formula for evaluating the effectiveness of interventions:

[0055] in, Represents the percentage of intervention effect. The baseline value represents the core indicators (such as liver stiffness) before intervention. This represents the measured value of the core indicator after a preset period. When... When the intervention is effective (indicators improve), the original intervention framework should be maintained; when When the intervention effect is generally poor, the specific parameters of the intervention measures such as diet and exercise should be adjusted; when If the intervention is ineffective (indicators deteriorate), the AI-assisted decision-making module is restarted to generate a new intervention plan based on the latest data.

[0056] After the optimized intervention plan is confirmed by the doctor, it is pushed to the patient's mobile device, and the patient's intervention record in the liver disease specialist data resource pool is updated at the same time. Through big data analysis, the system explores the correlation between different intervention plans and disease development patterns, providing data support for clinical research and prevention and control policy formulation.

[0057] III. Implementation of the Doctor-Patient Collaborative Interaction Mechanism:

[0058] The system incorporates collaborative interaction mechanisms between doctors and patients at each stage: precise screening, treatment intervention, outpatient management, and dynamic optimization. In the precise screening stage, doctors send screening notifications and precautions to patients via a PC-based doctor's workstation, while patients provide feedback on appointment times and supplementary information about their medical history via mobile devices. In the treatment intervention stage, doctors provide preliminary treatment plans for patients' reference, and patients provide feedback on medication concerns and difficulties in implementing lifestyle interventions. In the outpatient management stage, doctors send health inquiries based on monitoring data (such as "Have you experienced fatigue or abdominal distension recently?"), and patients provide real-time feedback. In the dynamic optimization stage, doctors provide suggestions for adjusting the treatment plan, and patients provide feedback on their experience with the adjusted plan.

[0059] Through the aforementioned two-way real-time interaction, doctors can promptly grasp the patient's actual situation, and patients can clearly understand the control requirements and key points of implementation, ensuring the pertinence and efficiency of the control process.

[0060] Achieving closed-loop management of chronic liver disease throughout its entire lifecycle and improving the precision of prevention and control: This technical solution constructs a collaborative architecture of "perception layer - data layer - application layer - presentation layer" through a liver care system for chronic disease prevention and control. Combined with sequentially linked stages of precise screening, diagnosis and intervention, outpatient management, and dynamic optimization, it breaks down the disconnect between inpatient treatment and outpatient monitoring in traditional liver disease management. The system builds a unified liver disease specialty data resource pool based on a multi-dimensional set of health data. Through risk assessment and AI-assisted decision-making modules, it achieves data-driven operation throughout the entire process from risk stratification to individualized treatment plan generation, effectively improving the early detection rate and targeted intervention of chronic liver disease, and reducing the risk of disease progression to liver fibrosis and liver cancer.

[0061] Optimizing screening and assessment processes reduces medical costs and patient burden: The precision screening phase follows the principle of "from basic to in-depth, from non-invasive to invasive," replacing some invasive liver biopsies with non-invasive testing equipment such as transient elastography, reducing patient trauma and pain, while also lowering the complexity of testing procedures and medical costs, making it suitable for large-scale application in primary care institutions. The introduction of risk scoring formulas and intervention effect evaluation formulas enables quantitative assessment of risk levels and intervention effects, avoiding subjective judgment errors and improving the scientific rigor of diagnostic and treatment decisions.

[0062] Strengthening outpatient management and doctor-patient collaboration to improve intervention adherence: Through multi-terminal collaboration between the mobile patient application, WeChat official account, and PC-based doctor workstation in the presentation layer, combined with personalized reminders and science popularization pushes from an AI-powered health manager, a real-time and efficient doctor-patient collaborative interaction mechanism has been constructed. Patients can easily upload home monitoring data and receive intervention guidance, while doctors can track patients' outpatient health status in real time and adjust plans accordingly. This addresses the pain points of low patient follow-up adherence and untimely information feedback in traditional outpatient management, significantly improving the efficiency and effectiveness of intervention plan implementation.

[0063] Ensuring data security and standardization to support clinical research and policy making: The dynamic desensitization middleware and blockchain traceability module at the data layer achieve privacy protection and access traceability for multi-dimensional health data sets, complying with medical data security standards. A liver disease specialty data model built based on health information exchange standards ensures data standardization and interoperability, breaking down information silos between medical data sources. The massive amounts of diagnostic, monitoring, and intervention effect data accumulated by the system can be analyzed using big data to uncover disease development patterns, providing solid data support for clinical research on chronic liver diseases, optimization of treatment guidelines, and the formulation of public health prevention and control policies.

[0064] Adapting to different scenarios and populations, enhancing the universality and accessibility of the technology: The system adopts a "cloud-edge-device" collaborative deployment model, which can be adapted to different application scenarios such as hospitals, community health institutions, and homes. Primary care institutions can use the system to obtain diagnostic and treatment support from higher-level hospitals, while higher-level hospitals can monitor the follow-up status of patients at the primary care level in real time, promoting the downward flow of medical resources. At the same time, individualized treatment plans and lifestyle intervention programs fully consider the individual physiological characteristics, complications, and lifestyle habits of patients, adapting to patient groups with different risk levels and underlying diseases, thus improving the universality and operability of the technical solutions.

[0065] Enhancing the efficiency and experience of doctor-patient interaction and optimizing the quality of medical services: The visual presentation function and encrypted account login mechanism of the display layer allow doctors to quickly access patients' dynamic health records, risk stratification reports, and intervention effect data through the PC-based doctor workstation, reducing data query and processing time; patients can conveniently receive treatment suggestions, intervention reminders, and science popularization content through mobile devices, clearly understanding their own health status and management requirements. This two-way, real-time doctor-patient collaborative interaction mechanism ensures that doctors have a comprehensive understanding of patients' conditions while also meeting patients' health needs and information access requirements, thereby improving the satisfaction and quality of medical services.

[0066] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A liver care system for chronic disease prevention and control, characterized by: The system comprises a perception layer, a data layer, an application layer, and a presentation layer, which are sequentially and collaboratively connected. The perception layer is used to comprehensively collect patients' in-hospital treatment data, home monitoring data, and health record data to form a multi-dimensional health data set. The data layer is used to standardize, clean, process for privacy and security, and store the multi-dimensional health data set in a structured manner to build a unified liver disease specialty data resource pool. Based on the liver disease specialty data resource pool, the application layer realizes four core functions: risk assessment, personalized intervention plan generation, full-cycle follow-up management, and targeted patient education. The presentation layer adapts to the access needs of different users, supports multi-terminal login, and presents the assessment results, intervention plans, and health data change trends output by the application layer in a visual form, providing data support for doctor-patient interaction.

2. The liver care system for chronic disease prevention and control according to claim 1, characterized in that: The perception layer includes in-hospital medical equipment, home IoT devices, and medical data sources. The in-hospital medical equipment includes transient elastography equipment and liver function testing instruments. The home IoT devices include smart bracelets and blood glucose and lipid monitors. The medical data sources include electronic medical records and hospital information systems.

3. The liver care system for chronic disease prevention and control according to claim 1, characterized in that: The data layer constructs a liver disease specialty data model based on health information exchange standards. The data layer is configured with a data cleaning module, a dynamic desensitization middleware, and a blockchain traceability module. The blockchain traceability module is used to realize full-process traceability of data access.

4. The liver care system for chronic disease prevention and control according to claim 1, characterized in that: The application layer's risk assessment module integrates a scoring system and a deep learning model. The deep learning model is used to integrate liver function data, liver stiffness data, and metabolic index data to achieve liver cancer risk prediction and liver fibrosis staging assessment.

5. A comprehensive management method for a liver care system for chronic disease prevention and control based on any one of claims 1-4, characterized in that: It includes a sequential and closed-loop precise screening stage, a diagnosis and treatment intervention stage, an outpatient management stage, and a dynamic optimization stage. The precise screening stage is used to conduct stratified testing for the general population and high-risk populations to complete the determination and risk stratification of chronic liver disease risk levels. The diagnosis and treatment intervention stage is based on the risk stratification results of the precise screening stage, combined with the patient's individual physiological characteristics, complication status and medical history information, to generate a suitable individualized treatment plan and lifestyle intervention plan; the outpatient management stage realizes continuous tracking of the patient's outpatient health status and early warning of abnormal conditions through remote data collection, real-time indicator monitoring and interactive health guidance. The dynamic optimization phase, based on the implementation effects of the diagnosis and treatment intervention phase and the monitoring data of the outpatient management phase, dynamically adjusts the treatment plan and intervention program to form a closed-loop management system covering the entire cycle of "screening-intervention-monitoring-optimization".

6. The whole-process management method for liver care system for chronic disease prevention and control according to claim 5, characterized in that: The precise screening phase follows the principle of moving from basic to in-depth and from non-invasive to invasive. Primary care institutions complete the initial screening through liver function tests and abdominal ultrasound. For high-risk individuals, transient elastography is used to conduct precise assessment of liver fibrosis. The liver care system for chronic disease prevention and control automatically integrates the screening data and generates risk stratification reports of low risk, medium risk, or high risk.

7. The whole-process management method for liver care system for chronic disease prevention and control according to claim 5, characterized in that: During the diagnosis and treatment intervention phase, doctors obtain patients' dynamic health records through the liver care system for chronic disease prevention and control. The AI-assisted decision-making module at the application layer recommends individualized treatment plans, including drug optimization and surgical suggestions, based on clinical guidelines and multi-omics data. At the same time, it generates lifestyle intervention plans related to dietary restrictions, smoking cessation, and alcohol limitation.

8. The whole-process management method for liver care system for chronic disease prevention and control according to claim 5, characterized in that: During the outpatient management phase, patients upload home monitoring data through the mobile terminal of the display layer. The liver care system for chronic disease prevention and control tracks changes in liver function and liver stiffness data in real time. If an abnormality occurs, it automatically triggers a doctor's alarm. The AI ​​health manager in the application layer pushes personalized science popularization content and intervention reminders.

9. The whole-process management method for liver care system for chronic disease prevention and control according to claim 5, characterized in that: A doctor-patient collaborative interaction mechanism is set up in the precise screening stage, the diagnosis and treatment intervention stage, the outpatient management stage, and the dynamic optimization stage. The doctor-patient collaborative interaction mechanism includes doctors pushing diagnosis and treatment suggestions, intervention requirements, and health inquiries to patients through the PC terminal of the display layer, and patients providing feedback on the implementation status, health needs, and abnormal symptoms through the mobile terminal of the display layer, forming a two-way real-time interaction to ensure the pertinence and efficiency of the management and control process.

10. The whole-process management method for liver care system for chronic disease prevention and control according to claim 5, characterized in that: The dynamic optimization phase updates the intervention plan at preset intervals based on the patient's treatment effect, indicator changes, and lifestyle adjustments. The liver care system for chronic disease prevention and control uses big data analysis to uncover disease development patterns and provide data support for clinical research and prevention and control policy formulation.

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