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 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 ...

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...

Background Socioeconomic exposures related to anaemia in Peruvian children have been modelled assuming additive or log-additive relationships, yet such approaches overlook the fact that illness ...

Explore how proteomics in biomarker discovery accelerates diagnostic assay development and improves clinical validation.

Background Remission and low-disease activity are recommended targets in systemic lupus erythematosus (SLE), yet many ...

Objectives To evaluate whether type 2 diabetes mellitus (T2DM) presence and severity are associated with differences in global and domain-specific cognitive function among US adults, using ...

Machine learning models are usually complimented for their intelligence. However, their success mostly hinges on one fundamental aspect: data labeling for machine learning. A model has to get familiar ...

When RL is paired with human oversight, teams can shape how systems learn, correct course when context changes, and ensure ...