Two-stage Federated Phenotyping and Patient Representation Learning.
Two-stage Federated Phenotyping and Patient Representation Learning.
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DOI：
10.18653/v1/w19-5030
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Miller T
中科院分区：
文献类型：
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作者：
Liu D;Dligach D;Miller T
在电子病历系统中，很大比例的医疗信息是非结构化文本格式的。从临床笔记中手动提取信息非常耗时。近年来，自然语言处理被广泛应用于医学文本的自动信息提取。然而，由于医疗文档的异质性和唯一性，在来自单个医疗保健提供者的数据上训练的算法是不可推广的并且容易出错。我们开发了一种两阶段的联合自然语言处理方法，该方法可以利用来自不同医院或诊所的临床记录，而无需移动数据，并使用肥胖和合并症表型作为医疗任务来展示其性能。这种方法不仅提高了特定临床任务的质量，而且促进了整个医疗系统的知识进步，这是学习型医疗系统的重要组成部分。据我们所知，这是联邦机器学习在临床NLP中的首次应用。
A large percentage of medical information is in unstructured text format in electronic medical record systems. Manual extraction of information from clinical notes is extremely time consuming. Natural language processing has been widely used in recent years for automatic information extraction from medical texts. However, algorithms trained on data from a single healthcare provider are not generalizable and error-prone due to the heterogeneity and uniqueness of medical documents. We develop a two-stage federated natural language processing method that enables utilization of clinical notes from different hospitals or clinics without moving the data, and demonstrate its performance using obesity and comorbities phenotyping as medical task. This approach not only improves the quality of a specific clinical task but also facilitates knowledge progression in the whole healthcare system, which is an essential part of learning health system. To the best of our knowledge, this is the first application of federated machine learning in clinical NLP.