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Stroke Diagnosis and Prediction Tool Using ChatGLM: Development and Validation Study.

Xiaowei Song¹, Jiayi Wang², Feifei He³

¹Department of Neurology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.

Journal of Medical Internet Research

|February 26, 2025

Summary

This summary is machine-generated.

A new large language model (LLM) accurately diagnoses stroke using clinical notes and noncontrast computed tomography (NCCT) reports. This AI tool shows high accuracy in identifying stroke types and guiding treatment, potentially reducing patient disability and mortality.

Keywords:

ChatGLM acute stroke diagnosis electronic health records generative language model large language model noncontrast computed tomography prediction tool primary care stroke stroke detection treatment

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Area of Science:

Artificial Intelligence in Medicine
Clinical Informatics
Neurology

Background:

Stroke is a leading cause of death and disability worldwide.
Accurate and timely diagnosis is crucial for effective stroke treatment and improved patient outcomes.
Current stroke diagnosis faces challenges, leading to discrepancies in care.

Purpose of the Study:

To develop and validate a large language model (LLM) for stroke diagnosis and prediction.
To integrate free-text electronic health records and noncontrast computed tomography (NCCT) reports for enhanced stroke detection.
To improve the accuracy and speed of stroke identification and guide recanalization therapy.

Main Methods:

Utilized the ChatGLM-6B large language model (LLM) for stroke diagnosis.
Employed instruction tuning and low-rank adaptation (LoRA) techniques for model optimization.
Trained and validated the LLM on a dataset of 1885 patients and externally tested on 335 patients from multiple hospitals.

Main Results:

The LLM achieved 99% accuracy in internal validation and up to 95.5% in external validation for stroke diagnosis.
Demonstrated high accuracy in differentiating ischemic stroke from hemorrhage (up to 100%) and identifying large vessel occlusions (up to 88.6%).
Showcased effectiveness in screening patients for intravenous thrombolysis (IVT) with accuracies up to 89.4%.

Conclusions:

An LLM integrating clinical text and NCCT reports can effectively identify strokes and inform recanalization therapy decisions.
The developed LLM shows significant potential to enhance stroke identification accuracy and reduce critical reperfusion times.
Further validation through widespread deployment is recommended to confirm the clinical utility of this AI-driven diagnostic tool.