Titlebook: Artificial Intelligence in Medicine; 22nd International C Joseph Finkelstein,Robert Moskovitch,Enea Parimbel Conference proceedings 2024 Th

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Patient-Centric Approach for Utilising Machine Learning to Predict Health-Related Quality of Life Chental effects on patients’ physical and psychological wellbeing. This study aims to apply machine learning (ML) models to patient-reported, clinical, and demographic data to predict changes in physical well-being, social functioning, role functioning, usual activities, and mobility at 6, 12 and 18?w

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Predicting Blood Glucose Levels with?LMU Recurrent Neural Networks: A Novel Computational Modelctions or insulin pumps. Consumer devices can forecast blood glucose levels by leveraging data from blood glucose sensors and other sources. Such predictions are valuable for informing patients about their blood glucose trajectory and supporting various downstream applications. Numerous machine-lear

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Prediction Modelling and Data Quality Assessment for Nursing Scale in a Big Hospital: A Proposal to dentification of patients at risk of prolonged hospitalization or difficult discharge, and enables an estimation of care complexity during hospitalization. Given the high predictive value of these scales, it is important that the measurements are reported precisely. In this paper, we provide a gener

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Reinforcement Learning with Balanced Clinical Reward for Sepsis Treatmenttional treatment approaches, primarily reliant on clinicians’ judgment and standard guidelines, frequently fail to deliver personalized care. Moreover, clinical decisions may vary considerably among healthcare providers managing identical patient cases. In this study, we propose an innovative method

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