优化人类人工智能的范式 协作临床编码
Yue Gao1,2,3, Yuepeng Chen1,2, Minghao Wang1,2
1School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China.
NPJ digital medicine
|December 20, 2024
概括
这项研究介绍了CliniCoCo,一个用于自动化临床编码 (ACC) 的循环中人框架. 它通过将人类编码器与深度学习相结合来提高效率,减少编码时间并提高电子医疗记录的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 改善临床文档 改善临床文档
背景情况:
- 手动临床编码耗时且容易出现错误.
- 自动化临床编码 (ACC) 提供了一个潜在的解决方案,但需要人类监督才能在现实世界中应用.
- 将人类专业知识与ACC系统集成至关重要,以最大限度地提高效率和准确性.
研究的目的:
- 提出和评估CliniCoCo,这是一种新的HITL框架,用于ACC系统和人类编码器之间的有效协作.
- 使用深度学习优化注释工作负载并提高编码过程的效率.
- 评估CliniCoCo对编码时间,准确性和专业编码人员绩效的影响.
主要方法:
- 开发了一个名为CliniCoCo的Human-in-the-loop (HITL) 框架,利用深度学习实现自动化临床编码 (ACC).
- 在框架内的注释,培训和用户交互阶段实施了协作策略.
- 使用来自中国医院的现实世界电子病历 (EMR) 数据集进行实验.
主要成果:
- 通过优化注释工作负载,CliniCoCo获得了0.80-0.84的F1分数.
- 该系统证明了能够将30%错误的代码的EMR的注释要求减半,最小的0.01F1减少.
- 人类评估显示,CliniCoCo减少了40%的编码时间,并提高了EMR错误的校正率,包括在识别缺失代码方面提高了三倍.
- 专业程序员的表现有了显著的改善,F1的分数从0.72.93提升到0.93以上.
结论:
- CliniCoCo框架有效地将自动化临床编码与人类专业知识相结合,提高EMR处理的效率和准确性.
- 与CliniCoCo一样,HITL方法对于优化实际医疗保健环境中的ACC系统至关重要.
- CliniCoCo在临床编码方面取得了重大进展,减少了工作量并提高了数据质量,以获得更好的医疗保健结果.
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