Children autoimmune encephalitis index data processing method and system based on mechanism information and medium

By processing data on childhood autoimmune encephalitis using a physical information neural network model based on mechanistic information, the problems of prediction accuracy and interpretability under sparse data conditions were solved, and the continuous prediction of the pathological process and accurate acquisition of inversion mechanism parameters were achieved.

CN122201725APending Publication Date: 2026-06-12HUNAN CHILDRENS HOSPITAL +1
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Patent Information

Application Number
CN202610306037.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-13
Publication Date
2026-06-12

AI Technical Summary

Technical Problem

Existing methods for processing clinical data on childhood autoimmune encephalitis suffer from poor data interpretability and low prediction accuracy, especially when sparse data conditions make it difficult to accurately fit the pathological process.

Method used

A physical information neural network model based on mechanistic information (PINN model) is adopted. The model is trained by window data processing and feature encoding, combined with mechanistic constraints. It uses sparse clinical time series data for prediction and outputs the predicted state estimation vector and inverted mechanism parameters.

Benefits of technology

Under sparse data conditions, the model can accurately reconstruct the continuous evolution trajectory of pathological indicators, providing highly interpretable and accurate reference data, and is suitable for predicting the course of autoimmune encephalitis in children.

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Abstract

The invention discloses a processing method and system for child autoimmune encephalitis index data based on mechanism information and a medium, and relates to the technical field of child autoimmune encephalitis data processing, and the method comprises the steps: training and obtaining a physical information neural network model; obtaining current sparse clinical time sequence data, performing window data processing and feature coding on the current sparse clinical time sequence data, and obtaining a current global semantic feature vector; inputting the current global semantic feature vector and the prediction time node into a physical information neural network model, and outputting a current prediction state estimation vector and an inversion mechanism parameter; according to the processing method for the child autoimmune encephalitis index data based on the mechanism information, when the current prediction state estimation vector and the inversion mechanism parameters obtained through prediction serve as reference data, the data interpretability is high, the data prediction precision is high, and the inversion mechanism parameters corresponding to the current sparse clinical time series data can be visually displayed.
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