问答系统从临床笔记中提取注射药物使用的信息
Maria Mahbub1, Ian Goethert2, Ioana Danciu3,4
1Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA. mahbubm@ornl.gov.
Communications medicine
|April 3, 2024
概括
这项研究开发了一个问答框架,从临床笔记中提取注射药物使用 (IDU) 信息. 该模型获得了78.03%的F1得分,改善了早期识别和减少风险人群的伤害.
科学领域:
- 临床信息学 临床信息学
- 自然语言处理自然语言处理.
- 公共卫生 公共卫生
背景情况:
- 注射药物使用 (IDU) 与死亡率和发病率的增加有关.
- 早期识别IDU对于实施降低危害干预措施至关重要.
- 从电子健康记录 (EHR) 中提取IDU数据是具有挑战性的,因为其在临床笔记中的非结构性.
研究的目的:
- 开发和验证一个问答 (QA) 框架,用于从非结构化的临床笔记中提取IDU信息.
- 解决缺乏验证的工具来识别IDU在EHR数据中的问题.
- 为了促进早期检测和干预,个人面临与注射剂有关的危害的风险.
主要方法:
- 设计了一个质量保证框架,包括创建一个黄金标准数据集和开发/测试一个质量保证模型.
- 来自1145名患者的2323份临床笔记的数据集来自美国退伍军人事务部 (VA) 企业数据仓库.
- 模型的性能被评估在暂时不再分发的数据上,以评估稳定性.
主要成果:
- 质量保证模型在严格答案匹配方面获得了51.65%的F1分数.
- 对于放松匹配,该模型获得了78.03%的F1得分,精度为85.38%和回忆为79.02%.
- 该模型在不同时间段的数据上展示了一致的性能.
结论:
- 已经引入了一个新的QA框架,用于从临床笔记中提取IDU信息.
- 该框架旨在提高识别注射毒品的人的准确性和效率.
- 该工具有可能促进知情型的患者护理和减少危害的策略.
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