aceMachine learning prediction of 30-day mortality in adults with a positive blood culture
Introduction A positive blood culture is a clinically heterogeneous finding that may represent true bloodstream infection, transient bacteremia or contamination-diagnostic uncertainty that complicates early clinical decision-making. We aimed to develop and evaluate machine learning models to predict 30-day all-cause mortality in adults with a positive blood culture. Methods Retrospective cohort study using MIMIC-IV. Adult hospitalizations with at least one positive blood culture were included (first positive cul…
In a retrospective study of 5,731 hospital episodes, a random forest model predicted 30-day death after a positive blood culture with 0.788 sensitivity and 0.707 specificity using routine data.
The models were built and tested on retrospective records from a single database (MIMIC-IV), so performance in real-time clinical use has not been shown.
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- Peer-reviewedEnfermedades infecciosas y microbiologia clinica (English ed.)2026-11-01
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Truvace Impact Record TRV-2026-2418, v1: “Machine learning prediction of 30-day mortality in adults with a positive blood culture.” Truvace, 2026-11-07. /record/TRV-2026-2418 (accessed on the day you copy it). sha256 d6d6dba8f265b018…
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