创建计算机辅助的ICD编码系统:性能指标的选择和ICD层次结构的使用
Quentin Marcou1, Laure Berti-Equille2, Noël Novelli3
1Aix-Marseille Université, Faculté des sciences médicales et paramédicales, Marseille, France; Aix-Marseille Université, UMR7020 CNRS, Laboratoire d'Informatique et Systèmes (LIS), Marseille, France.
机器学习通过改进国际疾病分类 (ICD) 编码来提高医疗保健. 神经网络的药物数据提供了高效的计算机辅助编码 (CAC) 系统,性能优于传统方法.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 临床数据分析 临床数据分析
背景情况:
- 国际疾病分类 (ICD) 编码对于计费和流行病学至关重要,但具有挑战性.
- 现有的自动ICD分类研究通常使用特定的模型和各种评估指标.
- 计算机辅助编码 (CAC) 系统旨在提高ICD编码的效率和准确性.
研究的目的:
- 使用通用方法开发和评估更有效的CAC系统.
- 探索ICD等级,药物数据和CAC前神经网络的实用性.
- 引入一种用于评估辅助编码系统的新型指标.
主要方法:
- 使用MIMIC-III临床数据库映射到OMOP数据模型的全面实验.
- 使用各种绩效指标进行评估,包括多任务,层次和不平衡的学习.
- 为ICD编码任务量身定制的新型指标的开发.
主要成果:
- 选择性ICD代码子集降低了检索性能.
- 在排名方面,NDCG和AUPRC的指标表现优于传统的基于F1的指标.
- 在不同的ICD级别上同时进行神经网络训练,提供了较小的排名优势和显著的运行时间优势.
- 阶层或阶级失衡纠正技术对ICD代码检索没有好处.
结论:
- 医疗处方是CAC系统的丰富数据来源,提供竞争性检索,计算负载低于基于文本的模型.
- 该研究强调了在评估CAC系统中选择指标的重要性.
- 挑战了现有的ICD代码为模型培训和评估提供子集的现有做法.
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