Imperial College London

Professor Anthony Gordon

Faculty of MedicineDepartment of Surgery & Cancer

Chair in Anaesthesia and Critical Care







ICUQueen Elizabeth the Queen Mother Wing (QEQM)St Mary's Campus






BibTex format

author = {Rawson, TM and Hernandez, B and Moore, L and Blandy, O and Herrero, P and Gilchrist, M and Gordon, A and Toumazou, C and Sriskandan, S and Georgiou, P and Holmes, A},
doi = {jac/dky514},
journal = {Journal of Antimicrobial Chemotherapy},
pages = {1108--1115},
title = {Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study},
url = {},
volume = {74},
year = {2019}

RIS format (EndNote, RefMan)

AB - BackgroundInfection 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.MethodsAn 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 160203 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.ResultsOne 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).ConclusionsAn SML algorithm was able to diagnose infection in individuals presenting to hospital using routinely available blood parameters.
AU - Rawson,TM
AU - Hernandez,B
AU - Moore,L
AU - Blandy,O
AU - Herrero,P
AU - Gilchrist,M
AU - Gordon,A
AU - Toumazou,C
AU - Sriskandan,S
AU - Georgiou,P
AU - Holmes,A
DO - jac/dky514
EP - 1115
PY - 2019///
SN - 0305-7453
SP - 1108
TI - Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study
T2 - Journal of Antimicrobial Chemotherapy
UR -
UR -
VL - 74
ER -