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PHeP: TrustAlert Open-Source Platform for Enhancing Predictive Healthcare with Deep Learning

Sandro Gepiro ContaldoDepartment of Computer Science, University of Turin, Turin, ItalyEmanuele PietropaoloCentre for Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Turin, Turin, ItalyLorenzo BosioDepartment of Computer Science, University of Turin, Turin, ItalySimone PerniceDepartment of Computer Science, University of Turin, Turin, ItalyIrene TerroneDepartment of Computer Science, University of Turin, Turin, ItalyDaniele BaccegaDepartment of Computer Science, University of Turin, Turin, ItalyYuting WangPolytechnic of Turin, Turin, ItalyRahul Kumar SahooPolytechnic of Turin, Turin, ItalyGiuseppe RizzoLINKS Foundation, Turin, ItalyAlessia ViscontiCentre for Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Turin, Turin, ItalyPaola BerchiallaCentre for Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Turin, Turin, ItalyMarco BeccutiDepartment of Computer Science, University of Turin, Turin, Italy
medRxivrepository2024en
ABI

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Abstract Predictive healthcare, a field revolutionized by the availability of large medical datasets and computational advancements, plays a pivotal role in enhancing patient care and healthcare system performance. Despite its significance, the training and deployment of predictive healthcare models present substantial challenges. To address these, we introduce the Predictive Healthcare Platform (PHeP), an open-source platform that simplifies the use of pretrained models. PHeP, developed under the TrustAlert project, offers an intuitive web-based graphical interface, making advanced predictive healthcare accessible to users without extensive computational skills or expensive computational resources. This paper presents the design and functionality of PHeP, demonstrating its effectiveness in predictive healthcare tasks, such as predicting re-hospitalization at three months using pre-trained BERT models.

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