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.

Both low and high levels of body roundness were linked to greater osteoarthritis risk among middle- and older-aged adults.

The promise of artificial intelligence in credit scoring is undeniable. By analyzing vast, non-traditional datasets from ...

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 ...

Domain adaptation may be a novel creative solution to predict infection risk in patients with chronic lymphocytic leukemia ...

Older adults experience inconsistent outpatient COVID-19 antiviral prescribing despite high risk for severe outcomes and hospitalization.

Billings Clinic investigators tracked trauma patients arriving directly from the scene versus patients transferred between ...

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.

TEM rolls out new AI tools across oncology, cardiology and mental health, accelerating its push to reshape MedTech innovation ...

While environmental factors are known to contribute to the risk of preterm birth, less is understood about how these exposures affect neonatal outcomes after delivery. To address ...

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...

Results in a population of 278 patients affirm statistically significant mortality reductionsBenefits observed across severity groups and in ...