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Using interpretable machine learning to predict bloodstream infection and antimicrobial resistance in patients admitted to ICU: Early alert predictors based on EHR data to guide antimicrobial stewardship
Research article (PLOS Digital Health, 2024) · cited 16× · AI/ML
Using interpretable machine learning to predict bloodstream infection and antimicrobial resistance in patients admitted to ICU: Early alert predictors based on EHR data to guide antimicrobial stewardship
Summary
Using interpretable machine learning to predict bloodstream infection and antimicrobial resistance in patients admitted to ICU: Early alert predictors based on EHR data to guide antimicrobial stewardship is a scholarly article[1].
Key Facts
Using interpretable machine learning to predict bloodstream infection and antimicrobial resistance in patients admitted to ICU: Early alert predictors based on EHR data to guide antimicrobial stewardship's instance of is recorded as scholarly article[2].
References
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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Using interpretable machine learning to predict bloodstream infection and antimicrobial resistance in patients admitted to ICU: Early alert predictors based on EHR data to guide antimicrobial stewardship. Retrieved May 24, 2026, from https://4ort.xyz/entity/using-interpretable-machine-learning-to-predict-bloodstream-infection-and-antimicrobial-resistance-in-patients-admitted-
MLA“Using interpretable machine learning to predict bloodstream infection and antimicrobial resistance in patients admitted to ICU: Early alert predictors based on EHR data to guide antimicrobial stewardship.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/using-interpretable-machine-learning-to-predict-bloodstream-infection-and-antimicrobial-resistance-in-patients-admitted-.
BibTeX@misc{4ortxyz_using-interpretable-machine-learning-to-predict-bloodstream-infection-and-antimicrobial-resistance-in-patients-admitted-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Using interpretable machine learning to predict bloodstream infection and antimicrobial resistance in patients admitted to ICU: Early alert predictors based on EHR data to guide antimicrobial stewardship}}, year = {2026}, url = {https://4ort.xyz/entity/using-interpretable-machine-learning-to-predict-bloodstream-infection-and-antimicrobial-resistance-in-patients-admitted-}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Using interpretable machine learning to predict bloodstream infection and antimicrobial resistance in patients admitted to ICU: Early alert predictors based on EHR data to guide antimicrobial stewardship — https://4ort.xyz/entity/using-interpretable-machine-learning-to-predict-bloodstream-infection-and-antimicrobial-resistance-in-patients-admitted- (retrieved 2026-05-24)
