Machine learning algorithm to predict all-cause mortality in asymptomatic healthy population: comparison with conventional risk prediction approach.

Back

ESC Premium Access

Machine learning algorithm to predict all-cause mortality in asymptomatic healthy population: comparison with conventional risk prediction approach.

Session: From telomere length to machine learning - News in risk prediction

Topic: Risk scores

Speaker: Doctor D. Han (Los Angeles, US)

Event: ESC Congress 2017

Congress Presentation

Add to my library

About the speaker

Doctor Donghee Han

Cedars-Sinai Medical Centre, Los Angeles (United States of America)

0 follower

9 more presentations in this session

Radiation and non-radiation risk factors including polymorphism rs966221 of phosphodiesterase 4D gene and coronary heart disease development in clean-up workers of the Chernobyl accident

Speaker: Doctor O. Bazyka (Kiev, UA)

Which elements of the complete blood count can predict the incidence of cardiovascular disease? the EPIC-NL cohort study

Speaker: Doctor C. Lassale (Barcelona, ES)

Early coronary arteries atherosclerosis in young patients with seronegative spondyloarthropathies, coronary computed tomography angiography study.

Speaker: Associate Professor R. Dabrowski (Warsaw, PL)

High-sensitivity troponin I predicts the risk of adverse outcomes in patients with chronic kidney disease

Speaker: Doctor P. Sandesara (Atlanta, US)

Evaluation of coronary artery calcium score for statin treatment strategy according to ESC guidelines in asymptomatic asian adults

Speaker: Doctor J. Lee (Seoul, KP)

Access the full session

From telomere length to machine learning - News in risk prediction

Speakers: Doctor D. Han, Doctor O. Bazyka, Doctor C. Lassale, Associate Professor R. Dabrowski, Doctor P. Sandesara...

Access

About the event

ESC Congress 2017

26 August - 30 August 2017

Sessions Presentations

Congress

ESC 365 is supported by

Explore sponsored resources