通过二次使用大型临床现实世界数据集进行多标签文本分类
Sai Pavan Kumar Veeranki1,2,3, Akhila Abdulnazar4, Diether Kramer1,5
1Steiermärkische Krankenanstaltengesellschaft m.b.H. (KAGes), Billrothgasse 18a, 8010, Graz, Austria.
Scientific reports
|November 6, 2024
概括
这项研究开发了一个应用程序,使用自然语言处理从操作笔记中预测医疗程序代码. 支持矢量机表现出强的性能,特别是在更长的报告中,有助于临床效率.
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.
- 机器学习 机器学习
背景情况:
- 程序编码对临床医生来说是一个很大的行政负担.
- 自然语言处理 (NLP) 为自动化编码任务提供了潜在的解决方案.
- 开发高效的工具可以减轻临床医生的工作量,提高医疗保健的效率.
研究的目的:
- 创建一个应用程序,通过分析临床操作笔记来预测医疗程序代码.
- 通过自动化编码辅助,简化临床医生的工作流程,提高行政效率.
- 评估各种机器学习模型的性能,以预测程序代码.
主要方法:
- 利用约35万个德国手术笔记的数据集进行模型调整.
- 模拟程序代码预测作为一个多标签分类任务.
- 基于变压器的模型 (medBERT.de,surgeryBERT.at) 与非上下文模型 (fastText,CNN,SVM,物流回归) 的比较.
主要成果:
- 支持矢量机 (SVM) 在超过512个序列的报告中实现了0.872的平均精度,超过了基于BERT的模型.
- 伯特模型 (medBERT.de,surgeryBERT.at) 和fastText实现了竞争性表现,平均平均精度分别为0.880和0.867.
- FastText表现出与BERT模型相似的性能,硬件要求较低,预测可解释性更好.
结论:
- 从操作报告中预测程序代码可以作为多标签分类任务来实现.
- 像SVM这样的经典机器学习方法可以非常有效地完成这项任务.
- 快速文本为基于BERT的模型提供了一个计算效率高的替代方案,用于程序代码预测和可解释性.
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