A machine learning model trained on EEG data from patients recovering from strokes helps predict how new patients will regain ...

A novel multi-task XGBoost model shows robust overall performance in predicting antimicrobial resistance in common gram-negative pathogens.

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...

Models using established cardiovascular disease risk factors had satisfactory predictive performance for 5-year CVD risk in ...

A deep learning model using retinal images obtained during ROP screening may be used to predict diagnosis of BPD and PH.

The ability to anticipate what comes next has long been a competitive advantage -- one that's increasingly within reach for developers and organizations alike, thanks to modern cloud-based machine ...

n this study, 773 untreated breast cancer patients from all over China were collected and followed up for at least 5 years. We obtained clinical data from 773 cases, RNA sequencing data from 752 cases ...

Background Suicide rates have increased over the last couple of decades globally, particularly in the United States and among populations with lower economic status who present at safety-net ...

More than a decade ago, researchers launched the BabySeq Project, a pilot program to return newborn genomic sequencing results to parents and measure the effects on newborn care. Today, over 30 ...

A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...