Machine learning predicts long-term adverse outcomes in neonatal bacterial meningitis patients, suggesting high-risk identification can improve care.
Machine learning models, particularly the superior-performing Random Forest, are proven to reliably predict long-term adverse outcomes in NBM patients, aiding in the identification of high-risk individuals. Further validation in broader cohorts is warranted to enhance generalizability and clinical applicability.
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Chen et al. (2025) studied this question.
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