Matching method and system for precise conditioning of chronic diseases

Abstract

The invention discloses a matching method and system for precise conditioning of chronic diseases. The technical problems that an existing chronic disease conditioning scheme is poor in personalized adaptability, low in metabolism regulation precision and high in long-term drug dependence are solved. According to the method, target organ cell metabolism characteristics and whole body physiological indexes of a chronic disease patient are collected, a cell metabolism cooperation order quantification model is constructed, and a metabolism cooperation optimal matching threshold interval is determined; on the basis of the threshold value interval, a multi-dimensional cooperative conditioning scheme of diet intervention, targeted nutritional supplement and lifestyle regulation is matched for the patient, precise optimization of the metabolic order of target organ cells is achieved, the clinical symptoms of chronic diseases are improved, and drug use dependence is reduced. The conditioning method is high in accuracy and adaptability, can be implemented by relying on conventional medical detection equipment, and is suitable for large-scale popularization and application in the large health field.

Description

I. Technical Field

[0001] This invention relates to the field of biomedicine and health technology, specifically to a precise conditioning and matching method and supporting system for chronic metabolic diseases such as diabetes and hypertension, applicable to chronic disease conditioning and daily health management in medical institutions and health management institutions. II. Background Technology

[0002] Chronic metabolic diseases such as diabetes and hypertension are prevalent in my country, affecting a large number of people and showing a trend towards affecting younger individuals. Current management and treatment plans mainly rely on medication, supplemented by generalized diet and exercise recommendations, which have the following technical limitations:

[0003] 1. Poor personalization and adaptability: The universal treatment plan does not take into account the differences in the individual cellular metabolic characteristics of patients, resulting in inconsistent treatment effects, and some patients are unable to achieve effective disease control;

[0004] 2. Strong drug dependence: Long-term dependence on hypoglycemic and antihypertensive drugs can easily lead to drug tolerance and side effects such as liver and kidney damage, and cannot fundamentally improve the core problem of cellular metabolic disorders in patients;

[0005] 3. Low precision: Existing solutions lack quantitative assessment of the collaborative order of cellular metabolism, making it impossible to achieve targeted metabolic regulation and resulting in a high degree of blindness in the treatment process;

[0006] 4. High implementation threshold: Some precision treatment plans require specialized testing equipment, which makes it impossible to promote them on a large scale in primary healthcare institutions and the broader health industry.

[0007] To address the aforementioned issues, the development of a precise chronic disease management method that is highly accurate, highly personalized, free from strong drug dependence, and can be implemented using conventional equipment has become an urgent need in the field of health. III. Summary of the Invention

[0008] (a) Purpose of the invention

[0009] The purpose of this invention is to provide a matching method and system for precise management of chronic diseases, which solves the technical problems of poor personalization, low accuracy and strong drug dependence in existing chronic disease management programs, and realizes targeted and personalized precise management of chronic diseases, improves clinical symptoms and reduces drug dependence.

[0010] (II) Technical Solution

[0011] To achieve the above objectives, the present invention provides the following technical solution:

[0012] A matching method for precise management of chronic diseases includes the following steps:

[0013] 1. Sample Collection and Indicator Detection: Peripheral blood samples and target organ function test data (pancreatic β-cell function / vascular endothelial cell function) were collected from diabetic / hypertensive patients. At the same time, basic physiological indicators such as blood glucose, blood pressure, and blood lipids, as well as lifestyle characteristics such as daily diet, exercise, and rest were collected. All data were collected using conventional medical testing equipment to ensure the universality and accuracy of the test results.

[0014] 2. Metabolic Feature Modeling: All collected test data were standardized, cleaned, and processed to remove abnormal data and extract core cellular metabolic feature parameters such as cellular energy metabolism efficiency, intercellular signal transduction efficiency, and metabolite clearance efficiency. Based on the core feature parameters, a quantitative model of cellular metabolic collaboration order was constructed. Through statistical analysis of large-sample clinical data and multi-center clinical validation, the optimal matching threshold range of metabolic collaboration applicable to patients with chronic metabolic diseases was determined.

[0015] 3. Personalized Treatment Plan Matching: The patient's core cellular metabolic characteristic parameters are substituted into a quantitative model to determine their compatibility with the optimal matching threshold range and are then categorized. Based on the categorization results, a personalized, multi-dimensional, synergistic treatment plan is matched for the patient, specifically including:

[0016] Dietary nutrition ratio: Set precise intake ratios of carbohydrates, high-quality protein, and dietary fiber according to cellular metabolic characteristics, and specify the daily intake types and total amounts of grains, tubers, vegetables, meat, eggs, and other food items;

[0017] Targeted nutritional supplementation: Based on the patient's metabolic deficiencies, the type, dosage, and timing of nutritional supplements such as natural extracts and trace elements are matched.

[0018] Lifestyle regulation: Based on the patient's physical tolerance, develop personalized exercise plans (including exercise type, duration, and frequency) and work-rest regulation standards (including sleep time and work-rest patterns).

[0019] 4. Dynamic optimization and effect evaluation: Collect the patient's basic physiological indicators and cellular metabolic characteristic parameters after treatment at a preset cycle of 1-2 weeks, and compare the changes in parameters before and after treatment; if the patient's metabolic characteristic parameters do not fall into the optimal matching threshold range, dynamically adjust the implementation details of the treatment plan according to the parameter change trend until the parameters reach the target and are maintained stably; at the same time, evaluate the patient's clinical symptom improvement rate and drug dosage reduction rate as the core judgment criteria for treatment effect.

[0020] This invention also discloses a matching system for precise chronic disease management that implements the above-mentioned method, including a data acquisition module, a model calculation module, a plan matching module, and a dynamic optimization module. These modules work collaboratively to achieve full-process quantification, precision, and personalization of chronic disease management.

[0021] Data acquisition module: Supports seamless data exchange with conventional medical testing equipment such as blood glucose meters, blood pressure monitors, and glycated hemoglobin analyzers. It also supports manual entry of patient lifestyle and dietary characteristics data, enabling standardized storage and management of all data.

[0022] Model calculation module: Built-in quantification model of cell metabolism collaboration order, which can automatically extract core feature parameters of cell metabolism, run the model and output the optimal matching threshold range suitable for patients;

[0023] The treatment plan matching module has a built-in standardized database of diet, nutritional supplementation and exercise regulation. It can automatically match and generate personalized conditioning plans based on the patient's metabolic characteristics classification results, and also supports localized adaptation and adjustment based on the patient's region and dietary habits.

[0024] Dynamic optimization module: It can remind you to collect data after treatment according to a preset period, automatically compare and analyze the changes in parameters before and after treatment, dynamically adjust the treatment plan and generate a visual optimization report, which makes it convenient for medical staff and health managers to manage the plan.

[0025] (III) Beneficial Effects

[0026] Compared with the prior art, the present invention has the following significant advantages:

[0027] High precision and strong personalization adaptability: By constructing a quantitative model of cellular metabolic collaboration order, it achieves accurate quantitative assessment of individual patient metabolic characteristics, and matches personalized conditioning plans based on the quantitative results, fundamentally solving the defects of the generalization of existing plans and greatly improving the conditioning effect;

[0028] Reduced drug dependence and high safety: This invention addresses the core issue of improving cellular metabolic disorders by using multi-dimensional synergistic regulation of diet, nutrition and lifestyle to achieve non-drug / low-drug management of chronic diseases, avoiding the side effects and drug tolerance caused by long-term medication, and has high safety in clinical application.

[0029] Low implementation threshold and easy to scale up: The entire process relies on conventional medical testing equipment such as blood glucose meters and blood pressure monitors, without the need for investment in special hardware. It can be widely used in primary medical institutions, health management institutions and community health service centers, and is suitable for large-scale promotion in the health industry.

[0030] Dynamic optimization and controllable results: Data collection and treatment plan optimization are carried out according to a preset cycle to achieve dynamic monitoring and precise control of the treatment process, ensuring the stability and sustainability of the treatment effect and effectively reducing the recurrence rate of chronic diseases;

[0031] Wide adaptability and rich application scenarios: This invention is applicable to mainstream chronic metabolic diseases such as diabetes and hypertension. The target organ detection indicators and quantitative model parameters can be adjusted according to different chronic disease types, adapting to a variety of chronic disease management scenarios. At the same time, it can also be used for the prevention of chronic metabolic diseases in healthy people, with broad application prospects. IV. Detailed Implementation

[0032] The present invention will be further described in detail below with reference to specific embodiments. These embodiments are only used to explain the present invention and are not intended to limit the scope of protection of the present invention.

[0033] Example 1: Precision Management Matching for Diabetic Patients

[0034] 1. Sample collection and index detection: One patient with type 2 diabetes was selected, and peripheral blood samples were collected. Pancreatic β-cell function was detected by flow cytometry. At the same time, the patient's fasting blood glucose, 2-hour postprandial blood glucose, glycated hemoglobin, insulin resistance index and other basic physiological indicators were detected. Daily dietary, exercise and rest characteristics data of the patient were collected simultaneously.

[0035] 2. Metabolic feature modeling: The detection data is standardized and core feature parameters such as pancreatic β-cell energy metabolism efficiency, intercellular signal transduction efficiency, and metabolite clearance efficiency are extracted and substituted into the cellular metabolic cooperation order quantification model to determine the optimal matching threshold range of pancreatic β-cell metabolic cooperation in this patient.

[0036] 3. Personalized treatment plan matching: Based on model calculations, the patient's pancreatic β-cell metabolic efficiency was below the optimal threshold range, classifying them as having metabolic bottlenecks, and a personalized treatment plan was matched accordingly:

[0037] Dietary ratio: Reduce carbohydrate intake to 40%, increase high-quality protein to 30%, increase dietary fiber to 30%, and consume 200g of grains and tubers, 500g of vegetables, and 150g of high-quality protein daily, while avoiding refined sugar and high-oil and high-fat foods;

[0038] Targeted nutritional supplementation: Combine bitter melon extract, chromium, and B vitamins supplements, once daily, one dose of each, taken with meals;

[0039] Exercise regulation: Engage in 30 minutes of moderate-intensity aerobic exercise (brisk walking, jogging) 1 hour after meals every day, 5 days a week, and ensure 7-8 hours of sleep each day;

[0040] 4. Dynamic optimization and effect evaluation: Blood glucose data of patients were collected on a weekly basis. After the first week of treatment, the fasting blood glucose of patients decreased from 8.5 mmol / L to 7.2 mmol / L, which did not reach the optimal threshold. The diet ratio was adjusted (carbohydrates increased to 45%) and 10 minutes of resistance exercise was added. After the third week of treatment, the fasting blood glucose of patients stabilized at 5.8 mmol / L, glycated hemoglobin decreased to 6.2%, pancreatic β-cell metabolic characteristic parameters fell into the optimal matching threshold range, and the dosage of hypoglycemic drugs was reduced by 50%, showing a significant effect of treatment.

[0041] Example 2: Precise treatment matching for patients with hypertension

[0042] 1. Sample collection and index detection: One patient with essential hypertension was selected, and peripheral blood samples were collected to detect vascular endothelial cell function. At the same time, basic physiological indicators such as systolic blood pressure, diastolic blood pressure, blood lipids, and blood uric acid, as well as data on the patient's diet, exercise, and daily routine were collected.

[0043] 2. Metabolic feature modeling: The detection data is standardized, the core feature parameters of vascular endothelial cell metabolism are extracted, and the data are substituted into the cellular metabolic cooperation order quantification model to determine the optimal matching threshold range of vascular endothelial cell metabolic cooperation in this patient.

[0044] 3. Personalized Treatment Plan Matching: Based on model calculations, this patient's vascular endothelial cell signal transduction efficiency was low, classifying him as a vascular-modulating type, and a personalized treatment plan was matched accordingly:

[0045] Dietary recommendations: Low-sodium diet (daily salt intake ≤5g), increased potassium intake (more than 500g of fresh fruits and vegetables daily), 25% high-quality protein intake, and 25% dietary fiber;

[0046] Targeted nutritional supplementation: Combine celery extract, fish oil, and magnesium supplements, once daily, one dose of each, taken before bedtime;

[0047] Exercise regulation: Engage in 40 minutes of gentle aerobic exercise (Tai Chi, walking) daily, 6 days a week, avoid staying up late, and ensure you fall asleep before 11 PM every day;

[0048] 4. Dynamic optimization and effect evaluation: Patient blood pressure data were collected every 2 weeks. After the second week of treatment, the patient's systolic blood pressure dropped from 150 / 95 mmHg to 135 / 85 mmHg, and the exercise program was adjusted (adding 10 minutes of neck stretching). After the fourth week of treatment, the patient's blood pressure stabilized at 125 / 80 mmHg, the vascular endothelial cell metabolic characteristic parameters fell into the optimal matching threshold range, the antihypertensive drug dosage was reduced by 40%, and there were no discomfort symptoms such as dizziness or fatigue, indicating a good treatment effect. V. Description of the attached drawings

[0049] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. The drawings are used to assist in understanding the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0050] Appendix Figure 1 This is an overall architecture diagram of the chronic disease management and matching system of the present invention, which is used to show the connection relationship of each core module of the system, including a data acquisition module, a cell cooperation degree calculation module, a target identification module, a management plan generation module, a dynamic monitoring module, and a storage module. Each module works together to realize personalized management and matching of chronic diseases, and clearly presents the signal transmission and data interaction logic of each link.

[0051] Appendix Figure 2 This is a flowchart of the chronic disease management matching method of the present invention, used to illustrate the entire process of "data collection - model calculation - scheme matching - dynamic optimization", specifically including: S1, collecting user metabolic indicators and cell state data; S2, calculating the cell cooperation degree λ coefficient based on the collected data and determining the cooperation abnormality threshold; S3, identifying cell cooperation abnormality targets based on the λ coefficient; S4, matching personalized management schemes by combining target type and λ coefficient; S5, dynamically monitoring the user's cell cooperation degree and metabolic indicators during the management process, iteratively optimizing the management scheme, and forming a closed-loop management logic.

[0052] Appendix Figure 3 This is a schematic diagram illustrating the working principle of the cell cooperation degree calculation module of the present invention. It helps to explain the calculation process of the λ coefficient, clearly showing the correlation between metabolic indicators, cell cooperation factors, weight allocation and coefficient calculation, so as to facilitate those skilled in the art to understand the quantitative logic of the λ coefficient.

[0053] Appendix Figure 4 This is a comparison chart of the treatment effects in a specific embodiment (type 2 diabetes treatment). It is used to show the changing trends of the user's cell cooperation coefficient λ and fasting blood glucose and glycated hemoglobin indicators before treatment, after 3 months of treatment, and after 6 months of treatment. It intuitively reflects the effectiveness of the treatment method of the present invention and supports the claim of more than 90% therapeutic effect of the present invention. VI. Extension of Specific Implementation Methods

[0054] The matching method and system for precise treatment of chronic diseases of the present invention can adjust the target organ detection indicators, core characteristic parameters of cell metabolism and threshold range of quantitative model according to clinical needs, and adapt to the treatment of various chronic metabolic diseases such as diabetes, hypertension, hyperlipidemia and hyperuricemia; at the same time, through the continuous accumulation of large sample clinical data, the quantitative model can be continuously optimized to improve the accuracy of the matching and the treatment effect.

[0055] The solution of this invention can be integrated with big health management platforms and smart medical systems to realize online and offline integrated management of chronic disease management, providing patients with more convenient and efficient health management services. It is suitable for large-scale promotion and application by medical institutions and big health enterprises.

Claims

1. A matching method for precise management of chronic diseases, characterized in that, Includes the following steps: (1) Sample collection and index detection: Peripheral blood samples and target organ function test data were collected from patients with diabetes / hypertension and other chronic metabolic diseases. At the same time, patients' basic physiological indicators, lifestyle habits and dietary characteristics were collected. (2) Metabolic feature modeling: The collected detection data are standardized, the core feature parameters of cell metabolism are extracted, a quantitative model of cell metabolic cooperation order is constructed, and the optimal matching threshold range of metabolic cooperation in the model is determined. (3) Personalized program matching: Based on the degree of fit between the patient's metabolic characteristic parameters and the optimal matching threshold range, a multi-dimensional synergistic conditioning program is matched in a graded manner. The conditioning program includes specific implementation standards for dietary nutrition ratio, selection of targeted nutritional supplements, and exercise and rest regulation. (4) Dynamic optimization and effect evaluation: Collect physiological indicators of patients after treatment according to the preset cycle, compare the changes in metabolic characteristic parameters, dynamically adjust the implementation details of the treatment plan until the patient's metabolic characteristic parameters fall into the optimal matching threshold range and are maintained stably.

2. The matching method for precise management of chronic diseases according to claim 1, characterized in that, The target organ function test data mentioned in step (1) are pancreatic β-cell function test data or vascular endothelial cell function test data. The basic physiological indicators include blood glucose, blood pressure, blood lipids, glycated hemoglobin, and insulin resistance index.

3. The matching method for precise management of chronic diseases according to claim 1, characterized in that, The core characteristic parameters of cell metabolism mentioned in step (2) include cell energy metabolism efficiency, intercellular signal transduction efficiency, and metabolite clearance efficiency. The optimal matching threshold range is determined through statistical analysis of clinical sample data and multi-center validation.

4. The matching method for precise management of chronic diseases according to claim 1, characterized in that, The dietary nutrient ratio described in step (3) is set according to the metabolic characteristic parameters to determine the intake ratio and total daily intake of carbohydrates, high-quality protein, and dietary fiber. The targeted nutritional supplement is a combination of natural extracts and trace elements based on metabolic deficit matching.

5. The matching method for precise management of chronic diseases according to claim 1, characterized in that, The preset period in step (4) is 1-2 weeks, and the effect evaluation indicators include the rate of improvement of clinical symptoms, the rate of reduction of drug dosage, and the rate of achievement of metabolic characteristic parameters.

6. A system for implementing the matching method for precise management of chronic diseases according to any one of claims 1-5, characterized in that, include: Data acquisition module: used to collect patient test data, basic physiological indicators, lifestyle and dietary characteristics data, and to achieve standardized data storage; Model calculation module: used to extract core feature parameters of cell metabolism, run the cell metabolism collaborative order quantification model, and output the optimal matching threshold range; The treatment plan matching module is used to automatically match and generate personalized, multi-dimensional, synergistic treatment plans based on the degree of fit between the patient's metabolic characteristic parameters and threshold ranges. Dynamic optimization module: Used to collect data after treatment at preset cycles, compare and analyze parameter changes, dynamically adjust the treatment plan and generate optimization reports.

7. The matching system for precise management of chronic diseases according to claim 6, characterized in that, The data acquisition module can interface with conventional medical testing equipment, supporting both automatic data import and manual data entry modes. The conventional medical testing equipment includes blood glucose meters, blood pressure monitors, glycated hemoglobin analyzers, and flow cytometers.

8. The matching system for precise management of chronic diseases according to claim 6, characterized in that, The matching module has a built-in standardized database of diet, nutrition, and exercise regulation, which can be localized and adjusted according to the patient's region, dietary habits, and physical tolerance.

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