Predicting Clostridioides difficile infection outcomes with explainable machine learning-Reference-Cited by-同舟云学术

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Predicting Clostridioides difficile infection outcomes with explainable machine learning

Published:2024-08 Issue: Volume:106 Page:105244
ISSN:2352-3964
Container-title:eBioMedicine
language:en
Short-container-title:eBioMedicine

Author:

Madden Gregory R.^ORCID,Boone Rachel H.^ORCID,Lee Emmanuel,Sifri Costi D.,Petri William A.

Publisher

Elsevier BV

Reference30 articles.

1. Trends in U.S. Burden of Clostridioides difficile infection and outcomes;Guh;N Engl J Med,2020

2. Clinical practice guideline by the infectious diseases society of America (IDSA) and society for healthcare Epidemiology of America (SHEA): 2021 focused update guidelines on management of Clostridioides difficile infection in adults;Johnson;Clin Infect Dis,2021

3. Validation of the SHEA/IDSA severity criteria to predict poor outcomes among inpatients and outpatients with Clostridium difficile infection;Stevens;Open Forum Infect Dis,2018

4. Validation of clinical risk models for Clostridioides difficile-attributable outcomes;Madden;Antimicrob Agents Chemother,2022

5. Derivation and validation of a simple clinical bedside score (ATLAS) for Clostridium difficile infection which predicts response to therapy;Miller;BMC Infect Dis,2013

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