针对儿科鼻炎的抗生素处方准则一致性的自动评估 临床注释
Davy Weissenbacher1, Lauren Dutcher2, Mickael Boustany3
1Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA, davy.weissenbacher@cshs.org.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 13, 2024
概括
生成型人工智能模型现在可以自动化对儿科鼻炎的抗生素处方适宜性审查,改善抗生素管理. 这项技术有助于实时临床决策和大规模监测.
科学领域:
- 医疗保健中的人工智能
- 自然语言处理应用程序
- 儿童传染病 儿童传染病
背景情况:
- 抗生素耐药性是一个全球性的健康问题,需要明智的处方.
- 儿童的鼻炎经常导致抗生素处方,通常是不适当的,因为症状与病毒感染重叠.
- 目前评估处方适当性的方法是手动的,耗时的,并且无法扩展.
研究的目的:
- 开发和评估一种自动化方法,用于评估使用自然语言处理 (NLP) 在儿科鼻炎中抗生素处方的适当性.
- 克服手动图表审查和ICD-10回顾分析代码的局限性.
- 利用生成性人工智能实现实时临床决策支持和抗生素管理.
主要方法:
- 在300个儿科鼻炎临床笔记上开发和训练生成模型.
- 采用快速工程技术,包括几次射击学习和思维链提示.
- 使用Llama 3 70B指令使用参数有效微调 (PEFT) 并评估Llama 3.1 405B指令.
主要成果:
- 短暂的学习和思维链促使模型性能得到改善.
- 拉玛3.1 405B-instruct模型在适当的处方中达到94.7%的准确率,在不适当的处方中达到66.2%的准确率.
- 在分类模两可的文档案例 (15注) 中发现了挑战.
结论:
- 生成型人工智能模型显示,在儿童鼻炎中自动化抗生素处方适当性评估具有前途.
- 开发的模型可以支持实时的临床决策和大规模的抗生素管理.
- 处理复杂或记录不完善的案件需要进一步细化.
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