Medicaid managed care organizations should prioritize children in low-opportunity neighborhoods to optimize health care utilization, improve minority health, and address health-related social needs.
A new machine learning model built using a simple and interpretable approach predicts in-hospital death in patients with acute liver failure and reveals top risk drivers.
Support vector machines improve classification by mapping inseparable signals into higher-dimensional spaces. Random forest models, through ensemble decision trees, increase robustness against ...
Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...
A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
TEM rolls out new AI tools across oncology, cardiology and mental health, accelerating its push to reshape MedTech innovation ...
Understanding the derivative of the cost function is key to mastering logistic regression. Learn how gradient descent updates weights efficiently in machine learning. #MachineLearning ...
This study explores the feasibility of using breathomic biomarkers analyzed by machine learning as a non-invasive diagnostic tool to differentiate between benign and malignant thoracic lesions, aiming ...
Background This study aimed to develop and validate a hybrid model integrating clinical features, vessel wall magnetic resonance imaging (VWMRI) characteristics, and radiomic features to predict the ...
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