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Cited by 299Machine Learning in HealthcareElectronic Health Records SystemsArtificial Intelligence in Healthcare

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Mining Electronic Health Records (EHRs): A Survey

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Yadav, Pranjul, Steinbach, Michael, Kumar, Vipin, Simon, Gyorgy·2018·ACM Computing Surveys
DOI 10.1145/3127881
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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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Metadata

Authors
Yadav, P., Steinbach, M., Kumar, V., & Simon, G.
Affiliations
  • University of Minnesota (US)
  • University of Minnesota - Twin Cities, MN, USA
ORCID
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2018-01-03
Volume
50
Issue
6
Pages
1-40
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3127881
OpenAlex
W2594685110
Cited by
299
References
432
Citations by year
2026: 12 · 2025: 36 · 2024: 37 · 2023: 39 · 2022: 45 · 2021: 43 · 2020: 27 · 2019: 35 · 2018: 16
FWCI
16.94
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM Computing Surveys (publishedVersion)
Funders
  • NIH (LM011972)
  • NSF (IIS-1344135 and IIS-1602394)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
electronic health records, data mining techniques, health data, disease prognosis, patient health management, research design, healthcare cost reduction

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