Real-World Deployment of a ML Pipeline for Pressure Wounds Prediction

Despraz, JérémieNektarijevic, S.Vancauwenberghe, LaureCito, PalomaMilosavljevic, StefanPerrin, SamiPouzols, SophieRiba‐Grognuz, OksanaMabire, CédricRaisaro, Jean Louis2025-05-282025-05-282025-05-262025-05-1510.3233/shti250280https://infoscience.epfl.ch/handle/20.500.14299/250849Hospital-acquired pressure injuries (HAPIs) are common complications that impact patient outcomes and strain healthcare resources. The Braden Scale is the standard tool for assessing HAPI risk, but it has limitations, including a high false-positive rate, potential oversight of subtle symptoms, and added workload for nurses. To address these issues, a fully automated AI clinical decision support system (CDSS) achieving 0.90 AUROC on retrospective data has been deployed.enHospital-acquired pressure injuriesMLOpsCDSSrisk assessmentReal-World Deployment of a ML Pipeline for Pressure Wounds Predictiontext::conference output::conference proceedings::conference paper