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Yearb Med Inform 2015; 24(01): 38-43
DOI: 10.15265/IY-2015-014
Original Article
Georg Thieme Verlag KG Stuttgart

Health Informatics via Machine Learning for the Clinical Management of Patients

D. A. Clifton
1   Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
,
K. E. Niehaus
1   Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
,
P. Charlton
1   Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
,
G. W. Colopy
1   Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
› Author Affiliations
Further Information

Publication History

13 August 2015

Publication Date:
10 March 2018 (online)

Also available at
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Summary

Objectives: To review how health informatics systems based on machine learning methods have impacted the clinical management of patients, by affecting clinical practice.

Methods: We reviewed literature from 2010-2015 from databases such as Pubmed, IEEE xplore, and INSPEC, in which methods based on machine learning are likely to be reported. We bring together a broad body of literature, aiming to identify those leading examples of health informatics that have advanced the methodology of machine learning. While individual methods may have further examples that might be added, we have chosen some of the most representative, informative exemplars in each case.

Results: Our survey highlights that, while much research is taking place in this high-profile field, examples of those that affect the clinical management of patients are seldom found. We show that substantial progress is being made in terms of methodology, often by data scientists working in close collaboration with clinical groups.

Conclusions: Health informatics systems based on machine learning are in their infancy and the translation of such systems into clinical management has yet to be performed at scale.

Keywords

Health informatics - data mining - information systems - electronic health records
 

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