aceApplication of machine learning to identify risk factors for bloodstream infections caused by different antimicrobial resistance phenotypes in chemotherapy patients
Bloodstream infections (BSIs) remain a major cause of morbidity and mortality in patients undergoing chemotherapy. The rising incidence of multidrug-resistant organisms (MDROs) further challenges the adequacy of empirical antimicrobial therapy. Therefore, this study aimed to identify features associated with different microbiological profiles of BSIs in chemotherapy patients using machine learning models. We retrospectively analyzed 1435 confirmed BSI episodes among 1220 patients treated at a tertiary cancer cen…
Application of machine learning to identify risk factors for bloodstream infections caused by different antimicrobial resistance phenotypes in chemotherapy patients: Model performance varied across pathogen groups, with the highest discrimination observed for CR-GNB (AUC-ROC 83.25%; 95% CI 73.91%-91.64%).
Evidence
- Peer-reviewedSupportive Care in Cancer2026-10-20
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Truvace Impact Record TRV-2026-2123, v1: “Application of machine learning to identify risk factors for bloodstream infections caused by different antimicrobial resistance phenotypes in chemotherapy patients.” Truvace, 2026-10-21. /record/TRV-2026-2123 (accessed at citation time). sha256 d6923d5b81af5f5e…
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