AIMC Topic: Morbidity

Clear Filters Showing 41 to 47 of 47 articles

A simulated annealing-based Bayesian network structure optimization framework for late morbidity prediction with a large prospective dataset.

Medical physics
BACKGROUND: Bayesian networks are seeing increased usage in healthcare, particularly for modeling complex treatment decisions under uncertainty. Bayesian networks offer significant advantages over classical machine learning and deep learning techniqu...

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...