Artificial intelligence (AI) models are promising tools for predicting antimicrobial susceptibility in gram-negative bloodstream infections (GN-BSI). Single-center study on hospitalized patients with GN-BSI, over 7-year period, aimed to predict resistance to fluoroquinolones (FQ-R), third…
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To assess if remote patient monitoring (RPM) programs for chronic conditions are positioned to advance equity through inclusive practices, 119 papers were evaluated against 11 multi-level equity parameters. Equity parameters were inconsistent and incompletely…
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Ultrasound assessment of fetal size and growth is the mainstay of monitoring fetal well-being during pregnancy, as being small for gestational age (SGA) or large for gestational age (LGA) poses significant risks for both…
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Millions of shift workers in the U.S. face an increased risk of depression, cancer, and metabolic disease, yet individual responses to shift work vary widely. We find that a conserved biological system of morning…
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Wearable medical devices are becoming increasingly common in everyday life, and thus is the reliance on them, the data they generate, and the resulting treatment plans. However, recent events in the supply chains of…
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Federated learning (FL) is advancing cancer research by enabling privacy-preserving collaborative training of machine learning (ML) models on diverse, multi-centre data. This systematic review synthesises current knowledge on state-of-the-art FL in oncology, focusing on…
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Generative artificial intelligence (GenAI) and large language models represent gains in educational efficiency and personalisation of learning. These are balanced against the considerations of the learning process, authentic assessment, and academic integrity. A pedagogical…
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This study introduces Glucose Level Understanding and Control Optimized for Safety and Efficacy (GLUCOSE), a distributional offline reinforcement learning algorithm for optimizing insulin dosing after cardiac surgery. Trained on 5228 patients, tested on 920,…
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This is a multi-institutional study to evaluate a head-and-neck CT auto-segmentation software across seven institutions globally. 11 lymph node levels and 7 organs-at-risk contours were evaluated in a two-phase study design. Time savings were…
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