Predicting Gram-negative bloodstream infection in elderly patients after isolation of GNB from non-blood specimens: a machine learning-based tool
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By
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Xinran Lin
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Daoming Zhang
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Ping Jiang
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Yongping Yao
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Yu Lv
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June 16, 2026
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0 min
3 Topic Commentaries
Intracranial Hemorrhages, Central Nervous System Infections, Machine Learning
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Dr. Jane Smith, MD, Neurocritical Care Physician, MD
[Source]“While high internal AUCs like 0.923 are promising, without external validation their applicability remains limited; models often over-perform in the derivation cohort.”
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Dr. Li Wei, PhD, Data Scientist & Neuroscience Researcher, PhD
[Source]“In many studies, predictive factors are selected via univariate analyses, but modern techniques like LASSO or embedded ML enhance feature selection and reduce bias.”
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Dr. Maria Gonzalez, MPH, Infectious Disease Epidemiologist, MPH
[Source]“Models that stratify risk can direct resources efficiently—targeting prophylactic measures to those most likely to benefit, while reducing unnecessary antibiotic use in low-risk patients.”
