A systematic literature review of machine learning applications for community-acquired pneumonia

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Lozano-Rojas, Daniel, Free, Robert C., McEwan, Alistair A. and Woltmann, Gerrit 2021. A systematic literature review of machine learning applications for community-acquired pneumonia. in: Lecture Notes in Electrical Engineering Springer.
AuthorsLozano-Rojas, Daniel, Free, Robert C., McEwan, Alistair A. and Woltmann, Gerrit
Abstract

Community acquired pneumonia (CAP) is an acute respiratory disease with a high mortality rate. CAP management follows clinical and radiological diagnosis, severity evaluation and standardised treatment protocols. Although established in practice, protocols are labour intensive, time-critical and can be error prone, as their effectiveness depends on clinical expertise. Thus, an approach for capturing clinical expertise in a more analytical way is desirable both in terms of cost, expediency, and patient outcome. This paper presents a systematic literature review of Machine Learning (ML) applied to CAP. A search of three scholarly international databases revealed 23 relevant peer reviewed studies, that were categorised and evaluated relative to clinical output. Results show interest in the application of ML to CAP, particularly in image processing for diagnosis, and an opportunity for further investigation in the application of ML; both for patient outcome prediction and treatment allocation. We conclude our review by identifying potential areas for future research in applying ML to improve CAP management. This research was co-funded by the NIHR Leicester Biomedical Research Centre and the University of Leicester.

Keywordscommunity acquired pneumonia; machine learning; CAP prediction
Year2021
Book titleLecture Notes in Electrical Engineering
Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021)
PublisherSpringer
ISBN9789811638800
ISSN1876-1119
Digital Object Identifier (DOI)https://doi.org/10.1007/978-981-16-3880-0_30
Web address (URL)http://hdl.handle.net/10545/625981
https://www.springer.com/tdm
http://creativecommons.org/publicdomain/zero/1.0/
hdl:10545/625981
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Publication dates15 Aug 2021
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Deposited07 Sep 2021, 11:09
Accepted2021
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ContributorsUniversity of Leicester, University of Derby and University Hospitals of Leicester NHS Trust, Leicester
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