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Machine learning model predicts ESBL risk to guide antibiotic selection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a machine learning model using XGBoost to predict the risk of ESBL-producing Enterobacteriaceae before culture results are available. This model, trained on data from 12 hospitals, aims to guide empiric antibiotic selection, potentially reducing unnecessary carbapenem use and its associated resistance. The model achieved high negative predictive value, suggesting it could safely spare carbapenems in non-ICU settings. SHAP analysis indicated that prior ESBL colonization was the most significant predictor, with factors like neighborhood deprivation having minimal impact on performance. AI

IMPACT This model could improve antibiotic stewardship and combat antimicrobial resistance by enabling more precise empiric treatment decisions.

RANK_REASON The cluster contains a research paper detailing a new machine learning model for medical risk stratification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine learning model predicts ESBL risk to guide antibiotic selection

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The cluster contains a research paper detailing a new machine learning model for medical risk stratification. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Aravind V. Kuruvikkattil, Lalitha Pranathi Pulavarthy, Rashmita Kudamala, Saptarshi Purkayastha ·

    Machine Learning for Pre-Culture ESBL Risk Stratification to Guide Empiric Antibiotic Selection: A 12-Hospital Study of Enterobacteriaceae Cultures

    arXiv:2609.05970v1 Announce Type: new Abstract: Empiric antibiotic therapy for suspected ESBL-producing Enterobacteriaceae must be selected 48-72 hours before culture results, forcing clinicians to choose between undertreating resistant infections and overusing carbapenems that d…

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