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dc.contributor.authorLozano-Rojas, Daniel
dc.contributor.authorFree, Robert C.
dc.contributor.authorMcEwan, Alistair A.
dc.contributor.authorWoltmann, Gerrit
dc.date.accessioned2021-09-07T11:09:20Z
dc.date.available2021-09-07T11:09:20Z
dc.date.issued2021-08-15
dc.identifier.citationLozano-Rojas, D., Free, R.C., McEwan, A.A. and Woltmann, G., (2021). ‘A Systematic Literature Review of Machine Learning Applications for Community-Acquired Pneumonia’. In Su, R., Zhang, Y., D. and Liu, H. (Eds.). ‘International Conference on Medical Imaging and Computer-Aided Diagnosis’. Singapore: Springer. pp. 292-30.en_US
dc.identifier.isbn9789811638800
dc.identifier.doi10.1007/978-981-16-3880-0_30
dc.identifier.urihttp://hdl.handle.net/10545/625981
dc.description.abstractCommunity 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.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.urlhttps://link.springer.com/chapter/10.1007%2F978-981-16-3880-0_30en_US
dc.rightsCC0 1.0 Universal*
dc.rights.urihttps://www.springer.com/tdm
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectcommunity acquired pneumoniaen_US
dc.subjectmachine learningen_US
dc.subjectCAP predictionen_US
dc.titleA systematic literature review of machine learning applications for community-acquired pneumoniaen_US
dc.typeBook chapteren_US
dc.identifier.eissn1876-1119
dc.contributor.departmentUniversity of Leicesteren_US
dc.contributor.departmentUniversity of Derbyen_US
dc.contributor.departmentUniversity Hospitals of Leicester NHS Trust, Leicesteren_US
dc.source.booktitleLecture Notes in Electrical Engineering
dc.source.booktitleProceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021)
dc.source.beginpage292
dc.source.endpage301
dcterms.dateAccepted2021
dc.author.detail300938en_US


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