ArticleFrontiers in pharmacology2026
Machine learning-based prediction of carbapenem-resistant
Article in Frontiers in pharmacology, 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
Objectives: This study aims to systematically analyze the risk factors for to carbapenem-resistant Methods: A single-center retrospective cohort study was conducted, including 491 patients with Results: Patients in the resistant group were older, had worse inflammatory and coagulation indicators, and exhibited significantly higher mortality rates from 30 to 180 days compared to the susceptible group ( Conclusion: The XGBoost machine learning-based prediction model effectively identifies the risk of CRKP infection and poor prognosis using five key variables (age, albumin, D-dimer, creatinine, and uric acid) for infection risk, demonstrating high clinical utility and providing data support for early intervention and individualized treatment.
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