在成精神病学中标准化临床笔记和信息提取的语言模型 - 一项实证研究
Haritha Gireesh1, Lekhansh Shukla2, Prakrithi Shivaprakash2
1Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, India.
Drug and alcohol review
|October 29, 2025
概括
微调的大型语言模型 (LLM) 通过校对和提取物质使用数据来改善成精神病学临床笔记. 这提高了文档的可读性,并促进了研究,即使在有限的计算资源.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 计算精神病学是一种计算精神病学.
- 医疗信息学 医疗信息学
背景情况:
- 电子健康记录 (EHR) 包含对成精神病学至关重要的非结构化的临床笔记.
- 临床笔记往往有错误,需要校对准确性和可读性.
- NLP方法和大型语言模型 (LLM) 可以适应临床笔记改进和数据提取.
研究的目的:
- 评估NLP方法用于校对临床笔记.
- 将LLM调整为校对和从EHR中提取与物质相关的信息.
- 为了比较适应的LLMs与现有解决方案和人类性能的性能.
主要方法:
- 从5年的成药物电子健康记录数据集 (2018-2023) 中分析了6500份临床笔记.
- 校对包括纠正拼写和扩展缩写;信息提取侧重于物质使用和自上次使用以来的时间.
- 微调的LLAMA-3.2-3b模型与Generative Pretrained Transformer-4-o进行比较,并通过面具偏好实验对人类进行校对.
主要成果:
- 基于LLM的校对大大提高了笔记可读性,减少了词汇库之外的单词.
- 精心调整的LLAMA模型在校对和信息提取方面都超过了Generative Pretrained Transformer-4-o.
- 在62%的试验中,人类评价者更喜欢模型更正的注释,而不是人类更正的版本 (p < 0.001).
- 信息提取实现了高整体性能 (平均F1 0.99),但在较罕见的物质类别中遇到了困难.
结论:
- 精心调整的LLM有效地标准化了临床笔记,并提取了成精神病学中的结构化数据.
- 这些功能提高了临床文档可读性,跨学科沟通和研究队列的创建.
- 自动信息提取可以减少工作人员的负担,并通过标记关键数据,如"自上次饮酒以来的时间"来改善患者的结果.
- 开源的LLM可以通过有限的计算资源来适应专门的任务,确保在消费级服务器上的数据隐私和安全性.
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