Heart rate (HR) estimation is crucial for early cardiovascular diagnosis, continuous monitoring, and various health applications. While electrocardiography (ECG) remains the gold standard, its discomfort and impracticality for continuous use have spurred the development…
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Sepsis, a life-threatening disease caused by infection, presents a major global health challenge due to its high morbidity and mortality rates. A rapid and precise diagnosis of sepsis is essential for better patient outcomes….
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Glaucoma is a progressive disease that can lead to permanent vision loss, making progression prediction vital for guiding effective treatment. Deep learning aids progression prediction but may yield unequal outcomes across demographic groups. We…
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Endovascular aortoiliac revascularization is a common treatment option for peripheral artery disease that carries non-negligible risks. Outcome prediction tools may support clinical decision-making but remain limited. We developed machine learning algorithms that predict 30-day…
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Early prediction of disability progression in multiple sclerosis (MS) remains challenging despite its critical importance for therapeutic decision-making. We present the first systematic evaluation of personalized federated learning (PFL) for 2-year MS disability progression…
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Effective hemodynamic management in the intensive care unit requires individualized targets that adapt to dynamic clinical conditions. We developed Dynamic Cohort Ensemble Learning (DynaCEL), a real-time framework that recommends personalized heart rate and systolic…
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Post-discharge management of coronary artery disease (CAD) remains clinically challenging, with digital healthcare’s efficacy underexplored. This study analyzed 16,797 CAD patients enrolled in the HeartMed Digital Management System (June 2018–September 2022), comparing outcomes between…
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The study assessed the feasibility of using synthetic data to fine-tune various open-source LLMs for free text to structured data conversation in radiology, comparing their performance with GPT models. A training set of 3000…
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Mobile health (mHealth) is gaining interest worldwide. This randomized trial aimed to test the effects of family companionship on the prevention of social frailty among Japanese individuals aged ≥40 years using a mHealth application….
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Routinely collected blood tests can reflect underlying pathophysiological processes. We demonstrate that the dynamics of routinely collected blood tests hold prediction validity in acute Spinal Cord Injury (SCI). Using MIMIC data (n = 2615) for modeling…
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