ArticleCancer medicine2026
Machine Learning for Prediction of High-Risk Infections in Patients With Cancer.
Article in Cancer medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeInfectious complications in patients with cancer are contributors to hospitalisations, treatment disruption, and mortality. This study aimed to develop machine learning (ML) models for risk stratification of infection-related hospitalisations (IRHs) among patients with cancer.
methodsAdult patients diagnosed with and treated for lymphoma, multiple myeloma (MM), chronic lymphocytic leukaemia (CLL), colorectal, or lung cancer from 2013 to 2023 in the North Denmark Region were included. Data from national cancer registries and patient health records were used. Serious infections were defined as sepsis, positive blood culture, intensive care admission, or death during hospitalisation. Models were developed using multiple ML methodologies across three different data setups: ML-using data up to hospital admission; ML
resultsAmong 9874 patients 2661 IRHs occurred, including 691 (21.5%) serious infections. On the held-out test set, ML achieved the best performance (ROC-AUC: 0.79) versus ML
conclusionThe ML models showed moderate performance in risk stratification of IRHs in patients with cancer. Such models could provide decision support in hospitalisation decisions and discharge.
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