临床数据仓库上的协作和增强隐私的工作流程:一个例子是开发自然语言处理管道来检测医疗条件
Thomas Petit-Jean1, Christel Gérardin1,2, Emmanuelle Berthelot3
1Innovation and Data Unit, IT Department, Assistance Publique-Hôpitaux de Paris, Paris, 75012, France.
概括
这项研究开发了一种自然语言处理 (NLP) 管道,用于检测法国临床笔记中的18种疾病. 协作,保护隐私的NLP管道实现了高准确性,证明了医疗保健中高效的AI模型开发.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 医疗保健中的人工智能
背景情况:
- 临床笔记包含有价值的患者数据.
- 准确提取医疗状况对于研究和患者护理至关重要.
- 在临床笔记中检测疾病的现有方法可能是有限的.
研究的目的:
- 开发和验证一种自然语言处理 (NLP) 管道,用于检测法国临床笔记中的18种疾病.
- 将查尔森指数中的16种并发病包括在内.
- 探索人工智能模型开发的协作和增强隐私的工作流程.
主要方法:
- 采用混合方法,将基于规则和机器学习算法结合起来,用于命名实体识别和实体资格.
- 采用了数以百万计的临床笔记预先训练的大型语言模型.
- 利用瘤学,心脏病学和风湿病学队列研究的注释数据.
- 设计了一种工作流程,以促进研究间的合作,同时确保数据隐私.
主要成果:
- 实现了95.7%的宏观平均F1得分,95.4%的积极预测值,96.0%的灵敏度和99.2%的特异性.
- 与替代技术和非协作设置相比,表现出卓越的性能.
- 通过安全注册表成功共享验证模型.
结论:
- 研究人员使用共同的临床数据仓库进行协作方法,使人工智能模型的开发,验证和部署能够高效和安全.
- 开发的NLP管道对于在临床笔记中检测疾病是高效和强大的.
- 这项工作突出了在医学研究中保护隐私的AI合作的潜力.
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