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Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study

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Title: Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study
Authors: Rawson, TM
Hernandez, B
Moore, L
Blandy, O
Herrero, P
Gilchrist, M
Gordon, A
Toumazou, C
Sriskandan, S
Georgiou, P
Holmes, A
Item Type: Journal Article
Abstract: Background Infection diagnosis can be challenging, relying on clinical judgement and non-specific markers of infection. We evaluated a supervised machine learning (SML) algorithm for diagnosing bacterial infection using routinely available blood parameters on presentation to hospital. Methods An SML algorithm was developed to classify cases into infection versus no infection using microbiology records and six available blood parameters (C-reactive protein, white cell count, bilirubin, creatinine, ALT and alkaline phosphatase) from 160 203 individuals. A cohort of patients admitted to hospital over a 6 month period had their admission blood parameters prospectively inputted into the SML algorithm. They were prospectively followed up from admission to classify those who fulfilled clinical case criteria for a community-acquired bacterial infection within 72 h of admission using a pre-determined definition. Predictive ability was assessed using receiver operating characteristics (ROC) with cut-off values for optimal sensitivity and specificity explored. Results One hundred and four individuals were included prospectively. The median (range) cohort age was 65 (21–98)  years. The majority were female (56/104; 54%). Thirty-six (35%) were diagnosed with infection in the first 72 h of admission. Overall, 44/104 (42%) individuals had microbiological investigations performed. Treatment was prescribed for 33/36 (92%) of infected individuals and 4/68 (6%) of those with no identifiable bacterial infection. Mean (SD) likelihood estimates for those with and without infection were significantly different. The infection group had a likelihood of 0.80 (0.09) and the non-infection group 0.50 (0.29) (P < 0.01; 95% CI: 0.20–0.40). ROC AUC was 0.84 (95% CI: 0.76–0.91). Conclusions An SML algorithm was able to diagnose infection in individuals presenting to hospital using routinely available blood parameters.
Issue Date: 1-Apr-2019
Date of Acceptance: 14-Nov-2018
URI: http://hdl.handle.net/10044/1/66436
DOI: https://dx.doi.org/10.1093/jac/dky514
ISSN: 0305-7453
Publisher: Oxford University Press (OUP)
Start Page: 1108
End Page: 1115
Journal / Book Title: Journal of Antimicrobial Chemotherapy
Volume: 74
Issue: 4
Copyright Statement: © 2018 Oxford University Press. This is a pre-copy-editing, author-produced version of an article accepted for publication in Journal of Antimicrobial Chemotherapy following peer review. The definitive publisher-authenticated version, T M Rawson, B Hernandez, L S P Moore, O Blandy, P Herrero, M Gilchrist, A Gordon, C Toumazou, S Sriskandan, P Georgiou, A H Holmes; Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study, Journal of Antimicrobial Chemotherapy, , dky514, is available online at: https://dx.doi.org/10.1093/jac/dky514
Sponsor/Funder: National Institute for Health Research
National Institute for Health Research
National Institute for Health Research
National Institute for Health Research
National Institute of Health Research Imperial Biomedical Research Centre
NIHR Invention for Innovation
Funder's Grant Number: HPRU-2012-10047
HPRU-2012-10047
II-LA-0214-20008
II-LA-0214-20008
WMNF_P46472
II-LA-0214-20008
Keywords: 1115 Pharmacology And Pharmaceutical Sciences
0605 Microbiology
1108 Medical Microbiology
Microbiology
Publication Status: Published
Embargo Date: 2019-12-22
Article Number: dky514
Online Publication Date: 2018-12-22
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering
Department of Medicine
Faculty of Medicine



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