AIMC Topic: Morbidity

Clear Filters Showing 41 to 46 of 46 articles

Identifying daily activities of patient work for type 2 diabetes and co-morbidities: a deep learning and wearable camera approach.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: People are increasingly encouraged to self-manage their chronic conditions; however, many struggle to practise it effectively. Most studies that investigate patient work (ie, tasks involved in self-management and contexts influencing such ...

Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk.

Age and ageing
BACKGROUND: ageing is an important risk factor for a variety of human pathologies. Biological age (BA) may better capture ageing-related physiological changes compared with chronological age (CA).

Genome-wide association study-based prediction of atrial fibrillation using artificial intelligence.

Open heart
OBJECTIVE: We previously reported early-onset atrial fibrillation (AF) associated genetic loci among a Korean population. We explored whether the AF-associated single-nucleotide polymorphisms (SNPs) selected from the Genome-Wide Association Study (GW...

Does Robotic Beating Heart Connector Totally Endoscopic Coronary Artery Bypass Bridge the Gender Gap in Coronary Bypass Surgery?

Innovations (Philadelphia, Pa.)
OBJECTIVE: Previous studies have shown that women carry a higher risk of morbidity and mortality after coronary artery bypass surgery. We investigated gender differences in risk factors and outcomes in our patients undergoing robotic beating heart co...