接近实时的急诊室综合征监控使用自然语言处理选文本:发烧检测的案例研究
Sedigh Khademi1,2, Christopher Palmer2, Muhammad Javed2
1Department of Paediatrics, University of Melbourne, Melbourne, Australia.
JMIR AI
|August 30, 2024
概括
基于变压器的模型显著改善了使用自然语言处理在急诊室检测发烧的情况. 这些先进的机器学习模型通过分析针对不良事件的分拣笔记来加强疫苗安全监测.
科学领域:
- 医疗信息学医学信息学
- 公共卫生监督是对公共卫生的监督.
- 医疗保健中的人工智能
背景情况:
- 免疫接种后检测不良事件对于公共安全至关重要.
- 发烧性是一种已知的幼儿疫苗反应.
- 早期识别安全信号有助于迅速干预.
研究的目的:
- 评估自然语言处理 (NLP) 和机器学习 (ML) 用于检测急诊室 (ED) 的发烧.
- 在这种情况下,确定对ML模型的最佳培训数据需求.
- 评估ML模型的实时监控部署要求.
主要方法:
- 开发了一个基线模式匹配方法.
- 在ED分类笔记中评估了各种ML模型用于发烧性的分类.
- 在训练期间与实时ED数据上的模型性能比较.
主要成果:
- 基于变压器的模型显著超过了标准的神经网络.
- 变压器模型在检测发烧方面表现出卓越的有效性.
- 最好的模型是资源高效的,适合部署.
结论:
- NLP,特别是大型语言模型 (LLM),为综合征监测提供了巨大的好处.
- LLM 作为高度有效的分类器,用于识别不良事件.
- 通过LLM文本生成能力,可以提高培训数据的质量和多样性.
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