关键词: 缺血性卒中; 大血管闭塞; 大语言模型; 推理

Abstract: Objective  To explore the feasibility and accuracy of using the large language model ChatGLM for the identification and diagnosis of large vessel occlusion in patients with acute ischemic stroke based on emergency electronic medical record information.
Methods  This study selected patients with acute ischemic stroke that occurred within 24 hours and were consecutively treated at the Emergency Neurology Department of Beijing Tsinghua Changgung Hospital, Tsinghua University, from January 2016 to January 2024. Large vessel occlusion and non-large vessel occlusion were distinguished according to head and neck vascular examinations (CTA/MRA/DSA). Based on the ChatGLM model, the inference processes and diagnosis results were given by using techniques such as prompt function, instruction fine-tuning, and retrieval-augmented generation, and the accuracy, sensitivity, and specificity of different inference processes for determining large vessel occlusion were explored. This was uniformly implemented in Python to represent the model’s inference performance.
Results  A total of 935 patients with acute ischemic stroke were included, including 230 patients with large vessel occlusion. The diagnostic accuracy rates for large vessel occlusion using zero-shot learning, zero-shot learning + chain-of-thought, generative pre-trained transformer+chain-of-thought, few-shot learning, and few-shot learning + chain-of-thought were 36.1%, 52.1%, 73.0%, 72.6%, and 75.1%, respectively.
Conclusions  The large language model ChatGLM demonstrates a certain feasibility in case diagnosis and inference. It can identify and diagnose emergency large vessel occlusion strokes based on electronic medical record texts. The diagnostic accuracy using few-shot learning is significantly higher than zero-shot learning.

Key words: Ischemic stroke; Large vessel occlusion; Large language model; Inference