The ability to identify patients at increased risk for hereditary cancer has never been more achievable, yet it’s still far from routine.

The authors created a machine learning–based model to identify patients with major depressive disorder in the primary care setting at high risk of frequent emergency department visits, enabling ...

Failing to associate the right patient with the appropriate action, referred to as wrong-patient errors, is a prevalent occurrence with potentially fatal consequences, according to a report from the ...

Background Chronic obstructive pulmonary disease (COPD) is a leading cause of global mortality, yet existing risk prediction models remain limited. This study aimed to develop and validate a ...

Thomas McElrath, MD, PhD, of the Department of Obstetrics & Gynecology at Brigham & Women’s Hospital, is the lead and corresponding author of a paper published in JAMA Network Open, "Utility of the US ...

Previous research has shown that stakeholders in the competitive alpine skiing communities consider risk management to be ...

Risk models at Credit Suisse had flagged the dangers before their $5.5 billion Archegos loss. Silicon Valley Bank's risk metrics showed clear warnings before their collapse. In both cases, ...

New data connector integrates EcoVadis’ ESG risk intelligence into Ivalua’s supplier management platform, enabling procurement teams to make faster, smarter, and more sustainable decisions EcoVadis, ...

David Benigson is CEO of Signal AI, a company using AI and media data to help executives cut through noise and drive actionable insights. Global threats now strike at warp speed. Yet some companies ...

For couples undergoing IVF, there is a need to know where the donor sperm came from, but at this fertility giant, there was a ...