美国麻醉师协会的预测 身体状态分类 从使用自然语言处理的手术前临床文本叙述
Philip Chung1, Christine T Fong2, Andrew M Walters2
1Department of Anesthesiology & Pain Medicine, University of Washington, 1959 NE Pacific Street, BB-1469, Box 356540, Seattle, WA, 98195-6540, USA. chungph@uw.edu.
BMC anesthesiology
|September 4, 2023
概括
自然语言处理模型可以从电子健康记录中预测患者的风险,而无需手动提取数据. 这种方法提高了患者的安全性,并通过分析临床笔记来简化医疗计费.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 临床决策支持 临床决策支持
背景情况:
- 电子健康记录 (EHR) 包含非结构化的文本注释,对于患者健康描述至关重要.
- 手动从EHR注释中提取数据以进行风险分层是耗时的.
- 直接从EHR注释中实现自动化术后风险分层的可行性尚未得到充分证实.
研究的目的:
- 评估使用自然语言处理 (NLP) 来预测美国麻醉学会身体状态分类 (ASA-PS) 的可行性.
- 为了比较不同的NLP模型和ASA-PS预测的文本部分的性能.
- 使用Shapley值来解释模型预测和分析与人类评估的差异.
主要方法:
- 对38566名患者进行了回顾性队列分析,其中61503名患者接受了手术.
- 四个NLP模型被训练在各种文本片段和EHR的完整笔记.
- 模型性能使用AUROC和AUPRC进行了评估;用Shapley值进行解释.
主要成果:
- 表现最好的模型实现了0.845的宏观平均AUROC (切断的笔记上的BioClinicalBERT) 和0.865 (全笔记上的fastText).
- 沙普利值为模型预测提供了人类可解释的解释.
- 错误分析表明,一些原始的ASA-PS分配中存在潜在的不准确性,模型做出了合理的预测.
结论:
- 文本分类模型可以准确地从自由形式的EHR文本预测患者的疾病严重程度,而无需手动提取.
- 这些模型可以作为术后环境中的患者安全工具,并减少手动图表审查.
- 沙普利特征归属为模型预测提供了临床上可以理解的解释.
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