自然语言处理用于从电子健康记录中检索观察性临床研究数据的实际用例:AssistMED项目
Cezary Maciejewski1,2,3, Krzysztof Ozierański4, Mikołaj Basza5
1First Department of Cardiology, Medical University of Warsaw, Warszawa, Poland
Polish archives of internal medicine
|March 19, 2024
概括
自然语言处理 (NLP) 准确且快速地从电子健康记录 (EHR) 中提取数据,用于临床研究. 这种NLP方法与手工方法相比,可以加快队列表征,提高研究效率.
科学领域:
- 计算语言学计算语言学
- 医疗信息学医学信息学
- 临床研究方法论临床研究方法论
背景情况:
- 电子健康记录 (EHR) 包含有价值的临床数据,但需要用于研究的手动提取.
- 从EHR中手动编码数据是耗时且昂贵的.
- 自然语言处理 (NLP) 提供了自动化文本分析功能.
研究的目的:
- 展示NLP的实际应用,用于描述和比较大型回顾性研究队列.
- 在心脏病学环境中评估NLP与人类检索的准确性和效率.
主要方法:
- 分析了来自10,314名心脏病患者的匿名出院文件.
- 临床特征是通过手动和NLP收集的,以便将其纳入CRAFT注册表.
- 对准确性,速度和对患者特征和风险评分的一致性进行了NLP和人类方法的比较.
主要成果:
- 在检测心房动 (AF) 中,NLP实现了99.93%的准确性,识别了3029名患者与人类的3030人相比.
- NLP分析比手动检索 (71小时12分钟) 快得多 (3小时15分钟).
- 在NLP和人类数据之间发现了很高的一致性,尽管每日剂量识别需要进一步验证.
结论:
- 在EHR中使用NLP可以加快对回顾性研究的数据采集.
- NLP提供准确的信息,提高临床研究的效率.
- 为了全面的NLP应用,可能需要对药物剂量等特定数据点进行进一步验证.
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