使用自然语言处理从心声图报告中检测双侧大动脉:退伍军人事务研究
Annie E Bowles1, Julie A Lynch2, Francisca Bermudez3
1VA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, Utah, USA.
JACC. Advances
|December 17, 2025
概括
一个新的自然语言处理 (NLP) 系统从心声图报告中准确地识别双主动脉 (BAV) 患者,使大规模的回顾性研究成为可能. 这个工具有助于诊断这种常见的先天性心脏缺陷.
科学领域:
- 心血管医学 心血管医学
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 双主动脉 (BAV) 是最常见的先天性心脏缺陷,由于各种症状,经常被诊断为晚期.
- 在2024年10月之前缺乏特定的BAV诊断代码,阻碍了对患者的追溯鉴定.
研究的目的:
- 开发和验证一种自然语言处理 (NLP) 系统,从心声图报告中自动提取心脏形态.
- 专门专注于准确检测双主动脉 (BAV).
主要方法:
- 使用MedSpaCy开发了一个基于规则的NLP系统来分析心声回声报告.
- 该系统接受了555份注释报告的训练,并对170份报告进行了验证,重点是门说明书结构识别.
主要成果:
- 该NLP系统在BAV检测方面表现出高性能,精度为 (0.925),灵敏度为 (0.939),F1得分为 (0.932).
- 应用到超过1400万个心声回声记录,该系统识别了83,461名BAV患者 (2.40%).
- 与ICD-10代码Q23.81观察到高一致性 (86.1%),手册审查证实了不一致的情况下的准确性.
结论:
- 这种NLP方法有助于从临床文本中大规模追溯识别BAV患者.
- 它建立了迄今为止最大的BAV队列,支持未来的心血管研究.
- 该系统有助于临床决策和及时诊断BAV.
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