Mining electronic health records (EHRs): A survey

Research output: Contribution to journalReview article

43 Scopus citations

Abstract

The continuously increasing cost of the US healthcare system has received significant attention. Central to the ideas aimed at curbing this trend is the use of technology in the form of the mandate to implement electronic health records (EHRs). EHRs consist of patient information such as demographics, medications, laboratory test results, diagnosis codes, and procedures. Mining EHRs could lead to improvement in patient health management as EHRs contain detailed information related to disease prognosis for large patient populations. In this article, we provide a structured and comprehensive overview of data mining techniques for modeling EHRs. We first provide a detailed understanding of the major application areas to which EHR mining has been applied and then discuss the nature of EHR data and its accompanying challenges. Next, we describe major approaches used for EHR mining, the metrics associated with EHRs, and the various study designs. With this foundation, we then provide a systematic and methodological organization of existing data mining techniques used to model EHRs and discuss ideas for future research.

Original languageEnglish (US)
Article number85
JournalACM Computing Surveys
Volume50
Issue number6
DOIs
StatePublished - Jan 2018

Keywords

  • Data mining
  • EHRs
  • Healthcare analytics
  • Healthcare informatics
  • Machine learning

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