Neurofibromatosis prediction method based on clinical medical information

CN122117419APending Publication Date: 2026-05-29FOURTH MILITARY MEDICAL UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
FOURTH MILITARY MEDICAL UNIVERSITY
Filing Date
2026-04-28
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Existing technologies are insufficient for accurately identifying risk transmission relationships between clinical features that evolve across multiple systems, time periods, and in a cascading manner in the clinical follow-up and risk assessment of neurofibromatosis. They also fail to quantify cross-domain latency and state transition probabilities, resulting in poor predictive stability.

Method used

A dynamic graph network method based on clinical medical information is adopted. By acquiring time-labeled multimodal data, anatomical system classification is performed, an initial multi-system prediction network graph is constructed, the asynchronous graph node feature aggregation rate is calculated, local feature diffusion processing is performed, the cross-domain evolution latency option weight and phenotypic cascade transition probability matrix is ​​determined, and the disease topological state information entropy is quantified to achieve early identification and stable prediction of neurofibromatosis.

Benefits of technology

It improves the completeness and continuity of clinical phenotype coverage, can uniformly express the correlation between multimodal clinical information, improves the identification of risk transmission relationships, and enables early identification and stable prediction of hidden cross-system risks.

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Abstract

The present application relates to the technical field of medical information processing and intelligent disease prediction, in particular to a neurofibromatosis prediction method based on clinical medical information. The method comprises the following steps: acquiring multi-modal clinical medical information with time labels; performing anatomical system classification processing, extracting clinical feature nodes and calculating correlation, and establishing an initial multi-system prediction network graph; determining the asynchronous graph node feature aggregation rate according to the feature update rate difference of adjacent nodes under different time labels, performing local feature diffusion processing, and determining a global multi-system prediction network graph; further determining the cross-domain evolution incubation period weight, the phenotype cascade transfer probability matrix and the disease topology state information entropy, and determining the neurofibromatosis prediction result accordingly. The present application solves the contradiction of long-term and ineffective consumption of a large amount of computing resources to cope with low-frequency but high-impact clinical events, and optimizes the trade-off relationship between risk identification accuracy and system response timeliness.
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