Mining Electronic Health Records (EHRs): A Survey
Open accessAbstract
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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EHR-based automated PHS is an effective way of preventing outbreaks by discovering utmost danger cases irrespective of merely reacting to outbreaks.
“It is an effective way of preventing outbreaks by discovering utmost danger cases irrespective of merely reacting to outbreaks [73].”
From Secondary Use of Electronic Health Record: Opportunities and Challenges (2020) · cites this paper as [73]
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- Authors
- Yadav, P., Steinbach, M., Kumar, V., & Simon, G.
- Affiliations
- University of Minnesota (US)
- University of Minnesota - Twin Cities, MN, USA
- Venue
- ACM Computing Surveys
- Published
- 2018-01-03
- Volume
- 50
- Issue
- 6
- Pages
- 1-40
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- 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 · 2017: 6 · 2016: 3
- FWCI
- 16.94
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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