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.

A machine learning model for prediction of preeclampsia risk using routinely collected data was feasible among pregnancies in ...

Researchers developed and validated ElasticNet machine learning models that predict 12-month MMSE and BADL outcomes in ...

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 enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.

A machine-learning model developed by Weill Cornell Medicine investigators may provide clinicians with an early warning of a complication that can occur late in pregnancy. Preeclampsia is a sudden ...

Machine learning can predict many things, but can it predict who will develop schizophrenia years before the average diagnosis time?

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

Statistical insights into machine learning analysis can help researchers evaluate model performance and may even provide new physical understanding.

Plants are constantly exposed to a wide array of biotic and abiotic stresses in their natural environments, posing ...