Benchmarking Machine Learning Architectures for Antimicrobial Stewardship in Pediatric ICUs
Antimicrobial stewardship (AMS) is critical in pediatric intensive care units (PICUs), where diagnostic uncertainty often drives broad-spectrum antibiotic use, increasing antimicrobial resistance and potential long-term harms. Machine learning offers a promising approach for identifying patient-level opportunities for stewardship interventions from electronic health record data, yet prior work has focused largely on adult populations and static tabular representations. We present a systematic benchmarking study of AMS intervention prediction in the PICU across the public Paediatric Intensive Care database a private cohort from the University Children's Hospital Zurich, Switzerland. We def...
| Authors | Niklas Raehse, Luregn J. Schlapbach, Daphné Chopard |
|---|---|
| Primary category | cs.LG |
| Published | 21 May 2026 |
| Last updated at source | 15 September 2026 |
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