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Cited by 795Machine Learning in HealthcareElectronic Health Records SystemsMedical Coding and Health Information

Next-generation phenotyping of electronic health records

Open access
Hripcsak, G., Albers, D. J.·2013·Journal of the American Medical Informatics Association
DOI 10.1136/amiajnl-2012-001145PMC PMC3555337
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Abstract

The national adoption of electronic health records (EHR) promises to make an unprecedented amount of data available for clinical research, but the data are complex, inaccurate, and frequently missing, and the record reflects complex processes aside from the patient's physiological state. We believe that the path forward requires studying the EHR as an object of interest in itself, and that new models, learning from data, and collaboration will lead to efficient use of the valuable information currently locked in health records.

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  • CONCERN EWS's methodological choices, including robust modeling of temporal data patterns and use of nursing assessment and observational data, allowed it to have accurate and robust predictive power and sufficient lead time to alter a patient's clinical trajectory.

    “Several methodological choices allowed CONCERN EWS to have accurate and robust predictive power and sufficient lead time to alter a patient’s clinical trajectory as we observed in this trial, including: (1) robust modeling of temporal data patterns40–42 (for example, time of day, day of week and patient hospital day) for health care processes (for example, nurses’ surveillance and medication”

    From Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial (2025) · cites this paper as [40]

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Authors
Hripcsak, G., & Albers, D. J.
Affiliations
Columbia University (US)
Corresponding author
Hripcsak, G.
ORCID
Venue
Journal of the American Medical Informatics Association
Publisher
Oxford University Press (OUP)
Published
2013-01-01
Volume
20
Issue
1
Pages
117-121
ISSN
  • 1067-5027 (ISSN-L)
  • 1527-974X
DOI
10.1136/amiajnl-2012-001145
PMC
PMC3555337
OpenAlex
W2121382432
Cited by
795
References
2
Citations by year
2026: 73 · 2025: 48 · 2024: 54 · 2023: 55 · 2022: 53 · 2021: 73 · 2020: 59 · 2019: 78 · 2018: 63 · 2017: 62 · 2016: 55 · 2015: 51 · 2014: 42 · 2013: 26 · 2012: 2
FWCI
38.11
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Journal of the American Medical Informatics Association (publishedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
U.S. National Library of Medicine (LM006910, R01LM006910)
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 › Medical Coding and Health Information
Keywords
electronic health records, phenotyping, clinical research data, data quality, missing data, data model, learning from data, health data utilization
MeSH terms
Diffusion of Innovation, Humans, Models, Theoretical, Quality Control, United States, Biomedical Research, Data Mining, Electronic Health Records

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