Method and system for optimizing dynamic diagnosis and treatment path of myasthenia gravis based on multi-modal fusion

By collecting and fusing multimodal data, constructing a three-dimensional dynamic feature matrix, training a multi-task deep network model, and optimizing the diagnosis and treatment pathway for myasthenia gravis, this approach solves the problems of rigid diagnosis and treatment pathways, reliance on experience, and lack of collaborative decision-making based on multimodal data in existing technologies. It achieves dynamic optimization of the diagnosis and treatment pathway and improves the treatment effect.

CN121723404BActive Publication Date: 2026-05-15湖南工商大学
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Patent Information

Application Number
CN202610204571.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-02-12
Publication Date
2026-05-15
Estimated Expiration
2046-02-12

AI Technical Summary

Technical Problem

The existing diagnostic and treatment pathways for myasthenia gravis lack dynamic adaptation mechanisms, rely on expert experience, fail to coordinate decision-making based on multimodal data, suffer from imbalances in hierarchical diagnosis and treatment, and lag in the evaluation of treatment effectiveness.

Method used

Collect multimodal data, extract and enhance features, construct a three-dimensional dynamic feature matrix, perform cross-modal feature fusion, train a multi-task deep network model, and optimize the diagnosis and treatment pathway.

Benefits of technology

It has enabled dynamic optimization of the diagnosis and treatment pathway for myasthenia gravis, improved the accuracy and scientific nature of diagnosis and treatment decisions, reduced irrational treatment behaviors, lowered costs, and increased treatment efficiency.

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Abstract

The application discloses a kind of myasthenia gravis dynamic diagnosis and treatment path optimization method and system based on multimodal fusion, the method includes: the multimodal data of myasthenia gravis patient is collected, and its feature extraction and enhancement are carried out to obtain multimodal feature;Based on multimodal feature, construct three-dimensional dynamic feature matrix and carry out cross-modal fusion, obtain target fusion feature;Target fusion feature is used to train multi-task depth network model, and the diagnosis and treatment path optimization model is obtained;According to preset interval, data is dynamically collected and input model, and diagnosis and treatment evaluation result is output to realize diagnosis and treatment path dynamic optimization.Due to the application, through multimodal fusion and dynamic optimization mechanism, break the limitation of traditional static path diagnosis and treatment, effectively excavate patient multidimensional disease characteristics, improve diagnosis and treatment evaluation accuracy, realize diagnosis and treatment path dynamic optimization, effectively reduce unreasonable diagnosis and treatment behavior, reduce diagnosis and treatment cost, improve efficiency, provide intelligent support for clinic, improve diagnosis and treatment effect.
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