利用自然语言处理来识别严重疾病通信的文档,用于减补性肝硬化患者
Lauren Smith1, Kate Sciacca2,3, Brigitte N Durieux2
1Department of Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
The American journal of gastroenterology
|November 14, 2025
概括
自然语言处理 (NLP) 有效地识别了不补偿性肝硬化患者的电子健康记录中的严重疾病通信 (SIC) 文档. 这项技术对于质量改进和临床试验来说是准确和可扩展的.
科学领域:
- 医疗信息学 医疗信息学
- 临床文档 临床文档
- 自然语言处理自然语言处理.
背景情况:
- 脱补偿性肝硬化 (DC) 患者的死亡率很高,需要改善严重疾病沟通 (SIC).
- SIC的电子健康记录 (EHR) 文档通常是无结构的,难以检索.
- 准确识别SIC对于患者护理和研究至关重要.
研究的目的:
- 评估自然语言处理 (NLP) 在临床笔记中识别SIC文档的有效性.
- 为了比较NLP的效率和准确性与SIC文档的手动图表审查.
- 评估NLP的可扩展性,用于DC患者的质量改善计划.
主要方法:
- 研究了一组成年DC患者的单中心队列,这些患者被评估为肝移植.
- 开发了一种半自动化的NLP方法,从住院和门诊EHR笔记中提取SIC文档.
- 将NLP的性能与四个SIC领域的黄金标准手动图表审查进行了比较.
主要成果:
- 在识别所有领域的SIC文档方面,NLP获得了高F1分数 (0.91-1.0).
- 与手册审查 (41.5分钟) 相比,NLP需要每名患者的时间显著减少 (6.8分钟).
- 很大一部分 (48%) 的患者缺乏记录的SIC.
结论:
- 对于DC患者来说,NLP是一种高效和准确的工具,用于识别DC患者在EHR中的SIC文档.
- 在质量改进计划和临床试验中,NLP可以在规模上实施.
- 这些发现支持NLP的使用,以加强在这个弱势群体中严重疾病沟通的管理.
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