High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP)
High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP)
复制标题
DOI：
10.1038/s41596-019-0227-6
复制
发表时间：
2019-12-01
期刊：
影响因子：
14.8
通讯作者：
Liao, Katherine P.
中科院分区：
文献类型：
--
作者：
Zhang, Yichi;Cai, Tianrun;Liao, Katherine P.
表型是疾病风险和结果的临床和遗传学研究的基础。与电子病历(EMR)数据相关联的生物库的增长既促进了对高效、准确和强大的方法的需求，也增加了对数百万患者表型的需求。使用EMR数据进行表型鉴定的挑战包括代码准确性的差异，以及识别算法特征和获得黄金标准标签所需的高水平人工输入。为了应对这些挑战，我们开发了PheCAP，一种高通量的半监督表型鉴定流水线。PheCAP从EMR中的数据开始，包括结构化数据和使用自然语言处理(NLP)从叙事笔记中提取的信息。这些标准化的步骤集成了自动化程序和机器学习方法，这些程序降低了人工输入的水平，用于算法培训。如果所有数据都可用，PheCAP本身可以在1-2天内执行；然而，时间在很大程度上取决于图表审查阶段，这通常需要至少2周。PheCAP的最终产品包括表型算法、所有患者的表型概率和表型分类(是或否)。
Phenotypes are the foundation for clinical and genetic studies of disease risk and outcomes. The growth of biobanks linked to electronic medical record (EMR) data has both facilitated and increased the demand for efficient, accurate, and robust approaches for phenotyping millions of patients. Challenges to phenotyping with EMR data include variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels. To address these challenges, we developed PheCAP, a high-throughput semi-supervised phenotyping pipeline. PheCAP begins with data from the EMR, including structured data and information extracted from the narrative notes using natural language processing (NLP). The standardized steps integrate automated procedures, which reduce the level of manual input, and machine learning approaches for algorithm training. PheCAP itself can be executed in 1-2 d if all data are available; however, the timing is largely dependent on the chart review stage, which typically requires at least 2 weeks. The final products of PheCAP include a phenotype algorithm, the probability of the phenotype for all patients, and a phenotype classification (yes or no).