Intelligent diagnosis and risk assessment method and system for cerebral apoplexy related to atrial fibrillation

By integrating multi-dimensional medical data and adopting intelligent model tuning mechanisms to generate structured databases and risk assessment reports, the problem of insufficient diagnostic accuracy of stroke related to atrial fibrillation is solved, and high-precision risk assessment and reliable clinical support are achieved.

CN120413041AInactive Publication Date: 2025-08-01THE SECOND AFFILIATED HOSPITAL TO NANCHANG UNIV
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Patent Information

Application Number
CN202510596654.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-09
Publication Date
2025-08-01
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

In the prior art, the intelligent diagnostic model of stroke related to atrial fibrillation lacks a dynamic tuning mechanism and multi-dimensional data collaborative verification, resulting in insufficient diagnostic accuracy and unreliable results, and the risk changes cannot be dynamically evaluated. The traditional method has a high misdiagnosis rate and cannot meet the clinical high accuracy needs.

Method used

By integrating multi-dimensional medical data, a structured database is generated using data quality verification, outlier value removal and missing value filling, machine learning models are trained and parameter joint search and convergence monitoring are carried out, and a variety of machine learning models are combined for cross-verification and survival probability analysis are analyzed to generate a risk assessment report.

Benefits of technology

It greatly improves the recognition accuracy of strokes related to atrial fibrillation, provides high-reliability support for clinical decision-making, and realizes dynamic risk assessment and accurate judgment of intervention timing.

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Abstract

The invention discloses an intelligent diagnosis and risk assessment method and system for cerebral apoplexy related to atrial fibrillation, and relates to the technical field of atrial fibrillation detection.The method comprises the steps that multi-dimensional data of atrial fibrillation patients in a clinical information system are integrated, a structured database is generated, and a standardized data set is output; a standardized data set is adopted to train a first machine learning model, and model performance is optimized through parameter joint search and a training set-verification set convergence dynamic monitoring mechanism; performing cross validation on a feature weight sorting result in the optimized diagnosis model and a clinical index risk association degree calculated by a second machine learning model to generate an interaction map; and based on clinical event data containing timestamps in the structured database, adopting a third machine learning model to extract time sequence features, and combining with a survival probability analysis model to generate a risk assessment report. According to the invention, through an intelligent model adjusting and optimizing mechanism, multi-dimensional medical data are effectively integrated, and the recognition precision of the atrial fibrillation related cerebral apoplexy is greatly improved.
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