在国家ENACT网络中开发和验证自然语言处理算法
Yanshan Wang1,2,3, Jordan Hilsman1,2, Chenyu Li1,2,3
1Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA.
Journal of clinical and translational science
|September 22, 2025
概括
ENACT NLP工作组在13个地点成功部署了自然语言处理基础设施,使得人们可以访问临床叙述进行翻译研究. 这表明了联合NLP部署的可行性,并强调了解决数据异质性的重要性.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 翻译研究是翻译研究.
背景情况:
- 电子健康记录 (EHR) 数据对于推进翻译研究和人工智能至关重要.
- 电子健康记录中的临床叙述包含有价值的信息,需要自然语言处理 (NLP) 来进行研究.
- ENACT网络的目标是通过57个临床和翻译科学奖 (CTSA) 枢纽提供对结构化EHR数据的访问.
研究的目的:
- 建立和运营ENACT NLP工作组,使NLP衍生的临床信息在ENACT网络中可访问和查询.
- 开发和验证NLP算法用于特定疾病背景下的各种临床任务.
- 扩展ENACT本体学和共同数据模型,以纳入NLP衍生数据,同时确保与SHRINE等现有研究网络的兼容性.
主要方法:
- 成立了ENACT NLP工作组,其中13个地点根据获得临床笔记,IT基础设施,NLP专业知识和机构支持而被选中.
- 将网站组织成五个重点小组,针对特定的临床任务,每个小组包括开发和验证网站.
- 扩展了ENACT本体学,标准化了NLP衍生数据,并使用开放健康自然语言处理 (OHNLP) 工具包进行了多站点评估.
主要成果:
- 实现了100%的站点保留,并在所有参与站点成功部署了NLP基础设施.
- 开发并验证了NLP算法,用于表型罕见疾病,健康的社会决定因素,阿片类药物使用障碍,睡眠和妄想.
- 在不同地点观察到性能变化 (F1分数为0.53-0.96),强调了数据异质对NLP模型概括性的影响.
结论:
- 在大型,联合的研究网络中展示了部署NLP基础设施的可行性.
- 焦点小组方法被发现比通用NLP策略更实用.
- 发现的关键挑战包括数据异质性和需要强大的协作治理,为其他网络为实现NLP用于翻译研究提供基础.
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