Development and validation of an interpretable machine learning-based model for predicting carbapenem-resistant <i>Acinetobacter baumannii</i> infection in postoperative ICU patients: a retrospective cohort study.

Front Cell Infect Microbiol · 2026 · PMC13057271 · PMID 41959558

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0.0870

closest p · 1.7× alpha

0.0870

boldest claim

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did not reach statistical significanceP = 0.087so close (0.05 < p ≤ 0.1)

Notably, RF exhibited the largest AUC decrease in the Top-5 feature model (0.866 to 0.856); however, this reduction did not reach statistical significance (P = 0.087) ( Supplementary Table 4 ).

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Development and validation of an interpretable machine learning-based model for predicting carbapenem-resistant &lt;i&gt;Acinetobacter baumannii&lt;/i&gt; infection in postoperative ICU patients: a retrospective cohort study.

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Development and validation of an interpretable machine learning-based model for predicting carbapenem-resistant <i>Acinetobacter baumannii</i> infection in postoperative ICU patients: a retrospective cohort study.