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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

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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
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Women’s & Reproductive Health
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Women’s & Reproductive Health
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Women’s & Reproductive Health
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Women’s & Reproductive Health
Role:
Author
More from this funder
Name:
Oxford Martin School
Grant:
R2015DeepMed
More from this funder
Name:
Rhodes Trust
Funding agency for:
Tran, J
More from this funder
Name:
Clarendon Fund
Funding agency for:
Tran, J
More from this funder
Name:
NIHR Oxford Biochemical Research Centre
Publisher:
Public Library of Science
Journal:
PLoS Medicine More from this journal
Volume:
15
Issue:
11
Article number:
e1002695
Publication date:
2018-11-20
Acceptance date:
2018-10-04
DOI:
EISSN:
1549-1676
ISSN:
1549-1277
Pubs id:
pubs:926370
UUID:
uuid:9034de39-afd2-432e-8308-a3a93ebfbe06
Local pid:
pubs:926370
Source identifiers:
926370
Deposit date:
2018-10-11

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