Back

Integrative machine learning model for predicting patient-oriented composite endpoints in acute myocardial infarction: a neuroendocrine biomarker approach

30 August 2024 (08:00 - 17:30)
Organised by: Logo
Congress Presentation Part of: Advancing cardiac care in acute coronary syndromes: from genetics to social determinants Epidemiology, Prognosis, Outcome ESC Professional Premium Access ESC Congress 2024 European Society of Cardiology

About the speaker

Photo
Doctor Ali Aldujeli

Bon Secours Hospital Limerick, Co. Limerick (Ireland)

8 More presentations in this session

Presentation thumbnail
Symptoms and ASCVD score fail to identify majority of the patients at risk of first myocardial infarction
Doctor A. Mueller (New York, US)
Abstract Slides
Presentation thumbnail
Corrected timi frame count : time to replace the TIMI flow?
Mr S. Rifqi (Semarang, ID)
Abstract Slides
Presentation thumbnail
Expression of cardiomyopathy-related genes in mononuclear blood cells predicts all-cause mortality in patients presenting with acute versus chronic coronary syndrome requiring revascularisation
Doctor A. Teren (Bielefeld, DE)
Abstract Slides Video
Presentation thumbnail
Undiagnosed Diabetes Mellitus is an important risk factor in secondary prevention after an acute coronary syndrome and warrants active screening
Ms E. Woelders (Nijmegen, NL)
Abstract Slides Video
Presentation thumbnail
Influence of social factors on prompt reperfusion in patients with ST-segment elevation myocardial infarction: results from the KRAMI multicenter registry
Doctor Z. Song (Busan, KR)
Abstract Slides Video

Access the full session

Session thumbnail
Advancing cardiac care in acute coronary syndromes: from genetics to social determinants

The Event

Event poster

ESC Congress 2024

30 August - 2 September 2024
You may be interested in
thumbnail
Congress Presentation
Development of a multidimensional data-driven prediction model and risk stratification for 1-year cardiac mortality in AMI patients
thumbnail
Congress Presentation
Machine learning for predicting the prognosis in patients with acute myocardial infarction complicated by cardiogenic shock
thumbnail
Congress Presentation
Development and validation of a machine learning-based explainable predictive model for long-term outcomes in patients with high bleeding risk undergoing percutaneous coronary intervention

ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources