系统评估常见的自然语言处理技术,以编码临床笔记
Nazgol Tavabi1,2, Mallika Singh1, James Pruneski1,2
1Boston Children's Hospital, Boston, MA, United States of America.
PloS one
|March 7, 2024
概括
传统的自然语言处理 (NLP) 方法有效地从操作笔记中预测外科手术程序代码 (CPT),优于复杂模型. 这些NLP技术提供了可解释性,可以减少医疗编码中的人为错误.
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.
- 计算语言学 计算语言学
背景情况:
- 准确的医疗编码对于医疗管理,研究和报销至关重要.
- 手动编码是劳动密集型的,容易出现人为错误.
- 自然语言处理 (NLP) 为自动化医疗编码提供了潜在的解决方案.
研究的目的:
- 评估常见的NLP技术,用于从操作笔记中预测当前程序术语 (CPT) 代码.
- 将传统NLP方法的性能与资源密集型模型 (如BERT) 的性能进行比较.
- 在NLP中引入分类任务的复杂度指标及其对数据集大小的影响.
主要方法:
- 各种NLP技术的综合性绩效评估.
- 分析重点是从操作笔记中获得的100个常见的肌肉骨CPT代码.
- 传统NLP方法与BERT的统计比较,包括AUROC和准确度指标.
主要成果:
- 传统的NLP方法在CPT代码预测方面显著优于BERT (P值=4.4e-17).
- 通过传统的NLP实现了高性能:平均AUROC为0.96和准确度为0.97.
- 证明了传统NLP模型的可解释性,这对于临床应用至关重要.
结论:
- 简单的,传统的NLP技术对于CPT代码预测非常有效和高效.
- NLP可以显著减少医疗编码错误,包括来自人类错误的错误.
- 提出的复杂性测量可以指导基于数据集特征的NLP模型的应用.
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