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Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records

Abstract:
Background Emergency admissions are a major source of healthcare spending. We aimed to derive, validate, and compare conventional and machine learning models for prediction of the first emergency admission. Machine learning methods are capable of capturing complex interactions that are likely to be present when predicting less specific outcomes, such as this one. Methods and findings We used longitudinal data from linked electronic health records of 4.6 mill... Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1371/journal.pmed.1002695

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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Women’s & Reproductive Health
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Women’s & Reproductive Health
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Women’s & Reproductive Health
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Women’s & Reproductive Health
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Women’s & Reproductive Health
Expand authors...
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Funding agency for:
Tran, J
More from this funder
Funding agency for:
Tran, J
NIHR Oxford Biochemical Research Centre More from this funder
Publisher:
Public Library of Science Publisher's website
Journal:
PLoS Medicine Journal website
Volume:
15
Issue:
11
Pages:
Article: e1002695
Publication date:
2018-11-20
Acceptance date:
2018-10-04
DOI:
EISSN:
1549-1676
ISSN:
1549-1277
Pubs id:
pubs:926370
URN:
uri:9034de39-afd2-432e-8308-a3a93ebfbe06
UUID:
uuid:9034de39-afd2-432e-8308-a3a93ebfbe06
Local pid:
pubs:926370

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