BACKGROUND: Renin-angiotensin system (RAS) inhibitors reduce risk of kidney failure in patients with chronic kidney disease, but worsen kidney function in heart failure patients, especially in those with chronic kidney disease. Less is known about ri...
BACKGROUND: Machine learning algorithms are essential for predicting severe outcomes during public health crises like COVID-19. However, the dynamic nature of diseases requires continual evaluation and updating of these algorithms. This study aims to...
BACKGROUND: Readmission within 30 days is a prevalent issue among elderly patients, linked to unfavorable health outcomes. Our objective was to develop and validate multimodal machine learning models for predicting 30-day readmission risk in elderly ...
BACKGROUND: Cognitive dysfunction is regarded as one of the most severe aftereffects following coronavirus disease 2019 (COVID-19). Eye movements, controlled by several brain areas, such as the dorsolateral prefrontal cortex and frontal-thalamic circ...
The field of Calculated Medicine has grown substantially over the last 7 decades. Comprised of objective, evidence-based medical decision tools, Calculated Medicine has broad application in medical practice, medical research, and health care manageme...
BACKGROUND: Atrial fibrillation and heart failure commonly coexist due to shared pathophysiological mechanisms. Prompt identification of patients with heart failure at risk of developing atrial fibrillation would allow clinicians the opportunity to i...
Machine learning has emerged as a significant tool to augment the medical decision-making process. Studies have steadily accrued detailing algorithms and models designed using machine learning to predict and anticipate pathologic states. The cardiac ...
BACKGROUND: Unnecessary laboratory tests contribute to iatrogenic harm and are a major source of waste in the health care system. We previously developed a machine learning algorithm to help clinicians identify unnecessary laboratory tests, but it ha...