Heyjun Park of the Johns Hopkins Bloomberg School of Public Health identifies biological and behavioral markers linked to heart disease, diabetes and pregnancy outcomes. She will speak Wednesday.

A deep learning algorithm that only uses data from mammogram images along with age may predict major cardiac events as accurately as traditional cardiovascular risk calculators, new data suggest. The ...

Pull requests help you collaborate on code with other people. As pull requests are created, they’ll appear here in a searchable and filterable list. To get started, you should create a pull request.

Background: Chronic Coronary Disease (CCD) is a leading global cause of morbidity and mortality. Existing Pre-test Probability (PTP) models mainly rely on in-hospital data and clinician judgment. This ...

A new machine learning model developed by The George Institute for Global Health can successfully predict heart disease risk in women by analyzing mammograms. The findings were published today in ...

10 The George Institute for Global Health, School of Public Health, Imperial College London, London, UK Background Cardiovascular risk is underassessed in women. Many women undergo screening ...

NFL Week 1 concludes with a Monday Night Football matchup at 8:15 p.m. ET between the Chicago Bears and Minnesota Vikings (-1, 43.5). Quarterback J.J. McCarthy will make his regular season debut after ...

Your waistline isn’t the only part of your body that could be putting your health at risk. A growing body of research suggests that neck size may also serve as an early warning sign, indicating a ...

New York, NY [August 28, 2025]—When genetic testing reveals a rare DNA mutation, doctors and patients are frequently left in the dark about what it actually means. Now, researchers at the Icahn School ...

This study aimed to develop a machine learning‐based model to predict the risk of major adverse cardiac events (MACE) in patients presenting to the emergency department (ED) with chest pain, for whom ...

This project aims to predict the presence of heart disease based on patient data using machine learning. The dataset includes various medical attributes such as age, sex, cholesterol levels, blood ...