Relating brain activity to behavior is an ongoing aim of neuroimaging research as it would help scientists understand how the brain begets behavior — and perhaps open new opportunities for ...

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

Benchmarking clinical reasoning and accuracy of large language models on breast oncology multiple-choice questions.

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

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

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 ...

PLSKB: An Interactive Knowledge Base to Support Diagnosis, Treatment, and Screening of Lynch Syndrome on the Basis of Precision Oncology We used an innovative machine learning approach to analyze ...

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 ...

Enterprise software is undergoing a major transformation as machine learning becomes deeply embedded into core digital products. Organizations are no longer using ML only for experimental analytics; ...