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使用ChatGLM进行中风诊断和预测工具:开发和验证研究

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

概括

一个新的大型语言模型 (LLM) 准确地使用临床笔记和非对比计算断层扫描 (NCCT) 报告来诊断中风. 这种人工智能工具在识别中风类型和指导治疗方面具有很高的准确性,有可能减少患者的残疾和死亡率.

科学领域:

人工智能在医学中的应用
临床信息学临床信息学
神经学神经学

背景情况:

在全球范围内,中风是导致死亡和残疾的主要原因.
准确和及时的诊断对于有效的中风治疗和改善患者结果至关重要.
目前的中风诊断面临挑战,导致护理中的差异.

研究的目的:

开发和验证用于中风诊断和预测的大型语言模型 (LLM).
整合自由文本电子健康记录和非对比计算机断层扫描 (NCCT) 报告,以增强中风检测.
为了提高中风识别的准确性和速度,并指导再生道治疗.

主要方法:

使用ChatGLM-6B大语言模型 (LLM) 进行中风诊断.
采用指令调整和低级调整 (LoRA) 技术进行模型优化.
在1885名患者的数据集上培训和验证了LLM,并对来自多家医院的335名患者进行了外部测试.

主要成果:

该LLM在内部验证中实现了99%的准确性,在中风诊断的外部验证中达到95.5%.
在区分缺血性中风和出血 (高达100%) 和识别大血管封闭 (高达88.6%) 方面表现出高度准确性.
在查静脉血栓溶解 (IVT) 患者中表现出有效性,精度高达89.4%.

结论:

整合临床文本和NCCT报告的LLM可以有效地识别中风,并告知再道治疗决策.
开发的LLM显示了显著的潜力,以提高中风识别的准确性,并减少关键的再注射时间.
建议通过广泛部署进行进一步的验证,以确认这种人工智能驱动的诊断工具的临床实用性.

关键词:

聊天GLMM 在线聊天剧烈的中风是急性中风.诊断诊断诊断诊断诊断诊断电子健康记录是电子健康记录.生成性语言模型的模型.大型语言模型无对比计算断层扫描无对比计算断层扫描预测工具是一个预测工具.主要护理是一级医疗保健.一次性中风中风中风中风中风电脑中风检测电脑中风检测治疗治疗治疗治疗治疗治疗

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