Yonsei University · 医学
Professor Hyo Suk Nam's research lab specializes in clinical and translational research focused on stroke outcomes, cardiovascular disease biomarkers, and predictive modeling. The lab investigates pathophysiological mechanisms such as trimethylamine N-oxide (TMAO) in atherosclerosis and post-stroke prognosis, integrating biomarkers with clinical data. Using advanced statistical methods like Bayesian networks, the lab develops interpretable, high-accuracy prediction models for functional outcomes and mortality after stroke, aiming to improve clinical decision-making. The research also emphasizes quality improvement in stroke care through implementation of clinical decision support systems.
Figures are computed from collected data and may differ slightly.
Trimethylamine N-oxide (TMAO) is produced when trimethylamine, a waste product of gut microbes, is converted via hepatic flavin monooxygenases. As TMAO is a potential causative factor in various cardiovascular diseases (CVDs) considerable research interest has arisen on its use as a biomarker. Higher TMAO levels are associated with future risk of both incident CVD in the general population and established CVD, including stroke. The addition of TMAO into models with traditional risk factors signi
ClinicalTrials.gov Identifier: NCT04205305.
Bayesian network is an increasingly popular method in modeling uncertain and complex problems, because its interpretability is often more useful than plain prediction. To satisfy the core requirement in medical research to obtain interpretable prediction with high accuracy, we constructed an inference engine for post-stroke outcomes based on Bayesian network classifiers. The prediction system that was trained on data of 3,605 patients with acute stroke forecasts the functional independence at 3
Long-term mortality in patients with an incomplete evaluation was quite high. Etiologic work-up helps to better define the stroke subtype and determine the prognosis.
Implementation of the CPOE-based team approach program significantly reduced time from emergency department arrival to evaluations and treatment.
Open papers in the app to read, cite, and organize with AI.
