The current assessment of Parkinson’s disease (PD) relies on dynamic motor tasks, limiting accessibility. This study aimed to propose an innovative approach to identifying PD and its stages using static standing balance and machine…
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People with a history of injecting drug use are a priority for eliminating blood-borne viruses and sexually transmissible infections. Identifying them for disease surveillance in electronic medical records (EMRs) is challenged by sparsity of…
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Stepped-care obesity treatments aim to improve efficiency by early identification of non-responders and adjusting interventions but lack validated models. We trained a random forest classifier to improve the predictive utility of a clinical decision…
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In this work we present OptAB, the first completely data-driven online-updateable antibiotic selection model based on Artificial Intelligence for Sepsis patients accounting for side-effects. OptAB performs an iterative optimal antibiotic selection for real-world Sepsis…
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We used natural language processing (NLP) in convergent mixed methods to evaluate young adults’ experiences with Call it a Night (CIAN), a digital personalized feedback and coaching sleep-alcohol intervention. Young adults with heavy drinking…
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Polysomnography, the gold standard diagnostic tool in sleep medicine, is performed in an artificial environment. This might alter sleep and may not accurately reflect typical sleep patterns. While macro-structures are sensitive to environmental effects,…
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Addressing drug-induced liver injury is crucial in drug development, often causing Phase III trial failures and market withdrawals. Traditional animal models fail to predict human liver toxicity accurately. Virtual twins of human organs present…
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Large language models (LLMs) are increasingly applied in medical documentation and have been proposed for clinical decision support. We argue that the future for LLMs in medicine must be based on transparent and controllable…
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Researchers from South Korea have built machine learning-based models that can predict mood episodes using only sleep and circadian rhythm data from wearable devices. The team comprised researchers from the Institute for Basic Sciences (IBS),…
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Reinforcement Learning (RL) is a machine learning paradigm that enhances clinical decision-making for healthcare professionals by addressing uncertainties and optimizing sequential treatment strategies. RL leverages patient-data to create personalized treatment plans, improving outcomes and…
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