An Efficient, Robust, and Customizable Information Extraction and Pre-processing Pipeline for Electronic Health Records
Eva Lee, Yuanbo Wang, Yuntian He, Brent M. Egan · 2019
Electronic Health Records (EHR) containing large amounts of patient data present both opportunities and challenges to industry, policy makers, and researchers. These data, when extracted and analyzed effectively, can reveal critical factors that can improve clinical practices and decisions. However, the inherently complex, heterogeneous and rapidly evolving nature of these data make them extremely difficult to analyze effectively. In addition, Protected Health Information (PHI) containing sensitive yet valuable information for clinical research must first be anonymized. In this paper we identify current challenges with obtaining and pre-processing information from EHR. We then present a comprehensive, efficient “pipeline” for extracting, de-identifying, and standardizing EHR data. We demonstrate the use of this pipeline, based on software from EPIC Systems, in analysing chronic kidney disease, prostate cancer, and cardiovascular disease. We also address challenges associated with temporal laboratory time series data and natural text data and develop a novel approach for clustering irregular Multivariate Time Series (MTS). The pipeline organizes data into a structured, machine-readable format which can be effectively applied in clinical research studies to optimize processes, personalize care, and improve quality, and outcomes.