A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...
Clinical and health care resource use burden were greater among patients with diagnosed hypereosinophilic syndrome or predicted hypereosinophilic syndrome via machine learning vs. those without the ...
Opioid overdoses continue to take a devastating toll across the United States. According to the U.S. Centers for Disease ...
Heart specialists at Mayo Clinic today presented new research at the 2026 Society of Thoracic Surgeons Annual Meeting that redo surgery for adults with congenital heart disease (CHD) remains high-risk ...
Heart specialists at Mayo Clinic today presented new research at the 2026 Society of Thoracic Surgeons Annual Meeting that ...
Can deep learning catch chronic illness before symptoms show? This article explores how time-aware neural networks are reshaping early detection and care planning for conditions like diabetes and COPD ...
Abstract: The recent AI development has provided effective solutions to address current problems and improve decision making process. The article takes a case study in Data and Information Centre ...
Background Suicide rates have increased over the last couple of decades globally, particularly in the United States and among populations with lower economic status who present at safety-net ...
Scientists at Mount Sinai have created an artificial intelligence system that can predict how likely rare genetic mutations are to actually cause disease. By combining machine learning with millions ...
Objective: To construct a prediction model for teicoplanin (TEIC) plasma concentrations through machine learning and deep learning techniques in patients with liver disease using real-world clinical ...
Background: Young adults aged 25 to 49 years, who are at the peak of their professional and familial responsibilities, face significant health and societal productivity challenges when affected by ...
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