通过使用自然语言处理在电子病历中自动识别手术后并发症
Harvey J Murff1, Fern FitzHenry, Michael E Matheny
1Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA. harvey.j.murff@vanderbilt.edu
JAMA
|August 25, 2011
概括
电子医疗记录的自然语言处理 (NLP) 提供了一种更敏感的方法来识别手术后的患者安全事件. 这种方法在检测并发症方面比传统的行政数据代码更准确.
科学领域:
- 医疗信息学医学信息学
- 临床信息学 临床信息学
- 医疗服务研究 医疗服务研究
背景情况:
- 传统的患者安全监测自动化方法依赖于行政数据代码.
- 电子医疗记录 (EMR) 通过自由文本数据分析提供了一个潜在的替代方案.
- 识别术后并发症对于患者的安全和质量改善至关重要.
研究的目的:
- 评估一种自然语言处理 (NLP) 搜索方法,用于在全面的EMR中识别术后外科并发症.
- 为了比较NLP的性能与现有的患者安全指标 (PSI),使用排放编码.
主要方法:
- 在6个退伍军人卫生管理局 (VHA) 医疗中心 (1999-2006) 接受住院手术的2974名患者的横截面研究.
- 确定了术后并发症,包括急性功能衰竭,深静脉血栓,肺栓塞,败血症,肺炎和心肌梗塞.
- 确定NLP方法的敏感性和特异性,并与PSI进行比较.
主要成果:
- 与PSI相比,NLP在识别急性功能衰竭 (82%对38%),肺炎 (64%对5%) 和败血症 (89%对34%) 等并发症方面表现出更高的敏感性.
- 对于大多数诊断,NLP和PSI都表现出高特异性.
- 对NLP的特定敏感性包括:急性功能衰竭 (82%),静脉血栓塞栓症 (59%),肺炎 (64%),败血症 (89%) 和术后心肌梗塞 (91%).
结论:
- 对EMR的NLP分析提供了一种比基于放电编码的PSI更敏感的方法来检测术后并发症.
- 虽然NLP表现出更高的灵敏度,但基于排放编码的PSI也表现出高的特异性.
- 这表明NLP是一种有价值的补充工具,可以加强手术环境中的患者安全监测.
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