Intelligent weight loss dynamic matching intervention method and decision-making system for obese patients in hospital based on multi-modal data fusion

By integrating multimodal data and dynamic modeling, a metabolic rate prediction model is constructed to optimize exercise and diet plans, providing personalized, graded interventions for obese patients. This solves the problem that existing technologies fail to consider individual metabolic differences and dynamic changes in metabolic state, achieving precise and dynamic full-cycle weight loss management.

CN120895173AInactive Publication Date: 2025-11-04KUNSHAN FIRST PEOPLES HOSPITAL
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Patent Information

Application Number
CN202510916470.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-03
Publication Date
2025-11-04
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Existing technologies do not fully consider individual metabolic differences and dynamic changes in metabolic status in weight loss interventions for obese patients, making it difficult to achieve precise and dynamic full-cycle weight loss management, especially in terms of exercise risk assessment and management of metabolic plateaus.

Method used

Using a multimodal data fusion approach, we collect and integrate multi-dimensional data from physiological, exercise, and psychological dimensions in real time. We construct a dynamic metabolic rate prediction model using LSTM neural networks and multimodal attention mechanisms. Combined with cardiac function analysis, we optimize exercise intensity and diet plans, provide personalized graded intervention strategies for different patients, and optimize postoperative rehabilitation through a neural stimulation system. We also monitor and adjust the weight loss process in real time.

Benefits of technology

It enables personalized, safe, and efficient weight loss intervention for obese patients, dynamically adjusts exercise and diet plans, reduces the risk of exercise injury, improves weight loss results, shortens abnormal response time, and ensures the effectiveness of weight loss management throughout the entire cycle.

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Abstract

The invention discloses a hospital obese patient intelligent weight loss dynamic matching intervention method and decision making system based on multi-modal data fusion, and relates to the field of medical health, and the method comprises the following steps: S1, carrying out the real-time collection and fusion of multi-modal data; s2, dynamic metabolism modeling and plateau period breakthrough; s3, generating a hierarchical intervention strategy; s4, performing psychological-physiological linkage intervention; and S5, performing real-time safety control and feedback optimization. By collecting multi-dimensional data, constructing the model, accurately predicting the metabolic rate, the metabolic plateau period and the sudden eating tendency, dynamically adjusting the exercise intensity and the diet scheme according to the conditions of different patients, triggering a low-intensity exercise mode for the obese patients with cardiac insufficiency and calibrating diet, ensuring the exercise safety, and improving the exercise efficiency. Operation suggestions are generated on the basis of the standard, precise preoperative evaluation is conducted, postoperative rehabilitation is optimized in combination with nerve stimulation data, dangerous postures are monitored, the sport injury risk is reduced, and personalized, safe and efficient intelligent weight reduction intervention for obese patients is achieved.
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