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 ...

Trustworthy AI isn’t just about predicting the right outcome; it’s about knowing how confident we should actually be.

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

AI & Society, states that algorithmic systems often construct competing but equally valid “model-worlds,” offering empirical support for a philosophical claim that evidence alone cannot uniquely ...

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

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

Tempus AI, Inc. (NASDAQ: TEM), a technology company leading the adoption of AI to advance precision medicine, today announced ...

Read more about From disease detection to biomass forecasting: AI improves aquaculture risk strategy on Devdiscourse ...

Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

Patient-reported outcome measures and clinical scales were ineffective in predicting responses to full-agonist opioids for chronic pain.

Children conceived through medically assisted reproduction have higher out-of-hospital healthcare utilization including mental health medications.