Trustworthy AI isn’t just about predicting the right outcome; it’s about knowing how confident we should actually be.
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 ...
Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
Explore a critique of the Citrini 2028 AI crisis forecast: how AI agents impact SaaS pricing, disruptors’ sustainability, and ...
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 ...
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 ...
Children conceived through medically assisted reproduction have higher out-of-hospital healthcare utilization including mental health medications.
Patient-reported outcome measures and clinical scales were ineffective in predicting responses to full-agonist opioids for chronic pain.
Dr. James McCaffrey presents a complete end-to-end demonstration of decision tree regression from scratch using the C# language. The goal of decision tree regression is to predict a single numeric ...
Results in a population of 278 patients affirm statistically significant mortality reductionsBenefits observed across severity groups and in ...
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