aceMachine learning models for predicting neonatal bacterial infections: a retrospective cohort study
Bacterial infections represent a critical threat to neonatal health, accounting for approximately 25% of neonatal mortality globally. Timely and precise diagnosis in infants aged 1 to 90 days is essential to facilitate rapid intervention and prevent severe complications. This study aimed to develop and evaluate machine learning (ML) models for the early, non-invasive prediction of bacterial infections using routine clinical data, maximizing clinical interpretability for point-of-care triage. Data from 306 infant…
L2-regularized logistic regression trained on routine non-invasive paraclinical markers achieved stable discrimination with minimal generalization gap and robust calibration for early prediction of bacterial infections in infants aged 1 to 90 days.
When tuned for ≥95% sensitivity screening, the models suffered a steep parallel decline in specificity and cannot safely eliminate the need for lumbar puncture, with top non-linear models showing severe training optimism.
High-sensitivity operation forces steep specificity loss, so model cannot be used as standalone rule-out to eliminate lumbar punctures and must remain a risk-tiering aid.
Evidence
- Peer-reviewedEuropean Journal of Pediatrics2026-08-14
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Truvace Impact Record TRV-2026-0783, v1: “Machine learning models for predicting neonatal bacterial infections: a retrospective cohort study.” Truvace, 2026-08-16. /record/TRV-2026-0783 (accessed at citation time). sha256 4efd0acbc0643b8f…
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