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

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 160203 individuals. A cohort of patients admitted to hospital over a 6month 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 72h 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 72h 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.