针对定制的大型语言模型的模型开发,用于在心理健康护理中的数字分拣协助
Niall Taylor1, Andrey Kormilitzin1, Isabelle Lorge1
1Department of Psychiatry, University of Oxford, Oxford, United Kingdom.
Artificial intelligence in medicine
|October 9, 2024
概括
大型语言模型 (LLM) 可以处理临床笔记,以帮助分类精神卫生保健转诊,减少等待时间. 这种人工智能方法为国家卫生服务 (NHS) 提供了实际的私人解决方案.
科学领域:
- 人工智能的人工智能
- 临床信息学 临床信息学
- 心理健康护理 心理健康护理
背景情况:
- 电子健康记录 (EHR) 包含非结构化的临床数据,特别是在心理健康方面.
- 英国国家卫生服务 (NHS) 面临着长期等待专家心理健康护理的等待列表,每月推37万至47万次.
- 有效的转诊分组对于及时的患者评估和治疗至关重要.
研究的目的:
- 评估大型语言模型 (LLM) 用于处理非结构化EHR数据,以协助精神卫生保健转诊分拣.
- 开发和评估基于LLM的方法来摄入可变长度的临床笔记.
- 提供与临床实践一致的AI驱动的分拣建议.
主要方法:
- 开发和评估了基于LLM的三个不同的方法,用于端到端摄入临床EHR数据.
- 专注于处理可变长度叙事自由文本临床笔记.
- 在单个GPU上实现了架构,用于实际的,资源有限的环境.
主要成果:
- 该LLM方法成功地提供了与当前临床实践相一致的分拣建议.
- 该模型的单个GPU实现证明了NHS设置的可行性.
- 该系统为分拣决策提供了大量临床笔记的高效处理.
结论:
- LLM提供了一个可行的解决方案,用于在EHR中处理非结构化临床数据,用于精神卫生保健分类.
- 开发的LLM架构适用于像NHS这样的资源有限的医疗保健系统.
- 这项技术有可能减少转诊等待时间,并改善分拣决策的合理性.
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