为医院获得的压力损伤开发住院电子病历表型:使用自然语言处理模型的案例研究
Elvira Nurmambetova1, Jie Pan1,2, Zilong Zhang1
1Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
JMIR AI
|June 14, 2024
概括
电子医疗记录 (EMR) 的自然语言处理 (NLP) 比单独的ICD代码更准确地检测出医院获得的压力损伤 (HAPI). 这有助于提高医疗保健质量和安全监督.
科学领域:
- 医疗信息学医学信息学
- 临床信息学是一种临床信息学.
- 医疗信息学 医疗信息学
背景情况:
- 使用行政数据 (ICD代码) 的医院获得的压力损伤 (HAPI) 监测通常会被延迟和低编码.
- 电子医疗记录 (EMR) 为更准确和及时的HAPI识别提供了一个潜在的解决方案.
- 自然语言处理 (NLP) 可以从EMR中的自由文本临床笔记中提取有价值的信息.
研究的目的:
- 为了证明基于EMR的表型算法,利用NLP,优于传统的ICD-10-CA代码用于HAPI检测.
- 为了验证通过NLP记录在护理笔记中的临床日志的准确性,用于HAPI识别.
- 提高急性护理机构HAPI监测的准确性和及时性.
主要方法:
- 在阿尔伯塔省卡尔加里的2015-2018年临床试验中,从EMR中的护理笔记中确定了HAPI病例.
- 文本分类模型 (随机森林,XGBoost,深度学习) 使用临床笔记的顺序前置选择来开发.
- 用灵敏度,特异性,正预测值,负预测值和F1得分来评估模型性能,并根据特异性调整值.
主要成果:
- 该研究分析了280名患者的数据,发现97名患者患有HAPI.
- 随机森林模型实现了0.464的灵敏度和0.984的特异性,F1得分为0.612.
- 机器学习模型表现出比基于ICD的算法更高的灵敏度,但没有显著的特异性损失.
结论:
- 基于EMR的NLP表型算法显著改善了HAPI检测,而不是单独使用ICD-10-CA代码.
- 在EMR中每日护理笔记是机器学习模型准确检测不良事件的丰富数据来源.
- 这种方法增强了自动化的医疗保健质量和安全监督系统.
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