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Mining Electronic Health Records (EHRs): A Survey

Authors:

Michael Steinbach,

Gyorgy SimonAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 50, Issue 6

Article No.: 85, Pages 1 - 40

https://doi.org/10.1145/3127881

Published: 03 January 2018 Publication History

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.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 50, Issue 6

November 2018

752 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3161158

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL

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Published: 03 January 2018

Accepted: 01 July 2017

Revised: 01 July 2017

Received: 01 March 2017

Published in CSUR Volume 50, Issue 6

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