Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

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.

Breakthrough AI foundation model called BrainIAC is able to predict brain age, dementia, time-to-stroke, and brain cancer ...

Interpretable AI model could offer new insights into why medicines cause certain side effects, helping to improve future drug safety predictions.

Unlike traditional testing, which requires hundreds or thousands of charge – discharge cycles, the model can estimate a new ...

Out-of-hospital cardiac arrest, or OHCA, is a leading cause of mortality worldwide and 90% of cases are fatal. Patients lose cardiac function and circulation, and every minute they remain untreated ...

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

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 ...

Scientists have created an AI model that forecasts moderate heat stress—a major precursor to coral bleaching—at sites along ...

Medical researchers at Mass General Brigham say the self-supervised foundational model can identify inherent features from ...

Emerging from stealth, the company is debuting NEXUS, a Large Tabular Model (LTM) designed to treat business data not as a simple sequence of words, but as a complex web of non-linear relationships.