The team utilized machine learning to analyze public data from the National Health and Nutrition Examination Survey.

Among patients with chronic noncancer pain, a novel machine learning model effectively predicts opioid use disorder risk.

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 ...

AI and machine-learning programs have entered medicine in many ways, including, but not limited to, helping to identify outbreaks of infectious diseases that may have an impact on public health; ...

The adoption of machine learning approaches in systematic reviews is fundamentally transforming evidence-based medicine. Traditionally, systematic reviews have involved painstaking manual screening of ...

Two new studies from the Department of Computational Biomedicine at Cedars-Sinai are advancing what we know about using machine learning and big data to improve health care and medical research. Both ...

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

A multi-institutional research team has demonstrated how artificial intelligence and machine learning can optimize therapy selection and dosing for septic shock, a life-threatening complication that ...

The Division of Infectious Diseases celebrates a paper published documenting antibiotic stewardship efforts here at UAB. The publication by first-author Rachael Lee, MD, with senior author Pete Pappas ...

CHARTwatch, a machine learning model, shows promise in reducing patient mortality and improving outcomes in hospital settings, according to new CMAJ study. Study: Clinical evaluation of a machine ...

Melkani's study, “Automated assessment of cardiac dynamics in aging and dilated cardiomyopathy Drosophila models using machine learning,” was published in Communications Biology (A Nature Portfolio ...