Machine learning-based prediction of bacterial infection foci: model development, calibration and uncertainty quantification using conformal prediction methods

Objective To develop and evaluate machine learning (ML) models capable of reliably predicting the focus of bacterial infections in hospitalised patients, addressing limitations of conventional microbiological diagnostics.Methods and analysis We conducted a retrospective study using data from 10 153 patients admitted to Rigshospitalet, Denmark, between 1 November 2019 and 3 June 2023. The dataset included microbiological findings, biochemical measurements and vital parameters. ML models were trained and evaluated, with outputs calibrated using Venn-ABERS calibration. Model uncertainty was quantified through conformal risk control to provide statistically robust uncertainty estimates.Results This study shows that the XGBoost model demonstrated the best performance, achieving a log loss of 0.209±0.006 (mean±SD) and an area under the receiver operating characteristic curve of 0.93±0.007. Incorporating conformal prediction techniques enhanced predictive reliability by combining high accuracy with calibrated probabilistic predictions and uncertainty estimates.Conclusion We find that ML models, particularly XGBoost combined with conformal prediction frameworks, can accurately predict the focus of bacterial infections while providing robust uncertainty quantification. This approach offers a promising addition to conventional diagnostic methods, potentially improving clinical decision-making.