BMJ digital health & AI · 2026
Machine learning-based prediction of bacterial infection foci: model development, calibration and uncertainty quantification using conformal prediction methods.
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Bahnsen Schmidt Jacob, Nielsen Karen Leth, Strunin Dmytro, Kirkby Nikolai S, Thomassen Jesper Q, Rasmussen Steen C, Frikke-Schmidt Ruth, Hertz Frederik Boetius, Engsig-Karup Allan Peter
Аннотация
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.
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.