一个多机构的自然语言处理管道,从电子健康记录中提取性能状态
Arash Maghsoudi1,2, Yvonne H Sada1,2, Sara Nowakowski1,2
1Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, USA.
Cancer control : journal of the Moffitt Cancer Center
|September 2, 2024
概括
一个新的自然语言处理 (NLP) 管道准确地从临床笔记中提取癌症患者的绩效状态 (PS). 该工具增强了用于临床决策和研究的数据,在各机构中显示出高准确度.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 在瘤学瘤学.
背景情况:
- 绩效状态 (PS) 对于癌症患者的评估至关重要,但往往被埋在临床笔记中.
- 自然语言处理 (NLP) 提供了一个从电子病历 (EMR) 中提取这些重要数据的解决方案.
研究的目的:
- 设计和验证一个多机构的NLP管道,自动从自由文本患者笔记中提取性能状态 (PS).
- 在现实世界的临床数据中评估管道的准确性和实用性.
主要方法:
- 开发了一个NLP管道,使用基于规则的方法进行后处理.
- 在哈里斯医疗系统 (HHS) 和退伍军人事务公司数据仓库 (VA-CDW) 的数据上进行了培训和验证管道.
- 测试了PS文档符合美国临床瘤学会 (ASCO) 质量指标,并分析了第四阶段非小细胞肺癌 (NSCLC) 报告中的差异.
主要成果:
- 实现了高准确性:在HHS数据上92%和VA数据上98.5%.
- 在IV期NSCLC患者中显示98.5%的准确性.
- 发现PS文档化率超过85%,超过ASCO质量指标,没有观察到差异.
结论:
- 开发的NLP管道有效地从各种医疗机构的自由文本注释中提取绩效状态.
- 该工具有可能用于纵向癌症数据注册表和改善临床决策.
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