As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
Real-World Deployment of a ML Pipeline for Pressure Wounds Prediction
Authors
Jérémie Despraz, Snežana Nektarijević, Laure Vancauwenberghe, Paloma Cito, Stefan Milosavljevic, Sami Perrin, Sophie Pouzols, Oksana Riba Grognuz, Cédric Mabire, Jean Louis Raisaro
Hospital-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.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.