联邦金融服务管理局 (FedFSA):跨机构的功能状态确认的混合和联合框架
Sunyang Fu1, Heling Jia2, Maria Vassilaki2
1Mayo Clinic, Rochester, MN, United States; University of Texas Health Science Center, Houston, TX, United States.
Journal of biomedical informatics
|March 8, 2024
概括
这项研究介绍了FedFSA,一个联合的NLP框架,有效地从电子健康记录中提取患者的功能状态. 联邦财政局的框架在多个机构的日常生活 (ADL) 活动受损的分类方面表现出卓越的表现.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 医疗信息学 医疗信息学
- 联邦学习学习 (Federated Learning) 是一种学习方式.
背景情况:
- 患者的功能状态,评估日常生活活动 (ADL) 的独立性,对于预测健康结果至关重要,特别是在老年人中.
- 功能状态信息通常在电子健康记录 (EHR) 中是无结构的,需要先进的计算方法来提取.
- 自然语言处理 (NLP) 提供了一个有效策划这些重要患者数据的解决方案.
研究的目的:
- 引入FedFSA,这是一个混合和联合的NLP框架,旨在从多机构的电子健康记录中提取功能状态信息.
- 开发一种计算方法来加速功能状态数据的处理,包括基本ADL (bADL) 和仪器ADL (iADL).
- 评估FedFSA框架在不同医疗机构的功能状态提取和损伤分类方面的表现.
主要方法:
- 开发了FedFSA,这是一个整合基于规则的信息提取 (IE) 的框架,以及用于ADL提取和损坏分类的联合BERT模型.
- 使用OHNLP Backbone用于基于规则的IE和Flower/PyTorch用于联合的BERT组件.
- 通过基于ICF定义的语料库注释生成黄金标准数据,涉及四个医疗机构.
主要成果:
- 联邦食品安全局在ADL提取 (bADL: 0.907-0.986; iADL: 0.825-0.951) 和损伤分类 (bADL: 0.722-0.954; iADL: 0.674-0.813) 获得了高的F1分数.
- 性能因ADL类别而异,洗衣和转移显示出高提取率,而食品准备和所使用率较低.
- 在FedFSA内部的联合学习优于对ADL提取受损的非联合方法.
结论:
- 联邦医疗健康管理局框架展示了联合学习的潜力,用于在EHR中进行可靠的功能状态提取和损伤分类.
- 在医疗保健中推进NLP应用方面,大规模的多机构合作开发工作至关重要.
- NLP技术可以显著提高存储在EHR中的功能状态数据的可访问性和实用性.
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