Back
Delegates Premium Access

Leveraging structural equation modeling and machine learning on large-scale metabolomic data to uncover atrial fibrillation risks

Session: From molecules to models: AI applications in omics and biomarkers
Topic: Machine Learning
Speaker: Mr J. Versnjak (Berlin, DE)
Event: ESC Digital & AI Summit 2025

Congress Presentation

Add to my library

Like

About the speaker

Mr Jakob Versnjak

Charite Campus Virchow Clinic, Berlin (Germany)
0 follower

5 more presentations in this session

Personalized prediction of aPTT responses to unfractionated heparin for ICU patients using a novel machine learning pipeline in a multi-cohort international study

Speaker: Mr M. Ali (Munich, DE)

Access
Abstract
Slides
Like
Thumbnail

Machine learning reveals metabolic and inflammatory predictors of exercise adaptation in HFpEF

Speaker: Doctor J. Marino (Greifswald, DE)

Access
Abstract
Slides
Like
Thumbnail

AI-enhanced risk prediction in acute heart failure: combining radiographic biomarkers with clinical data

Speaker: Assistant Professor K. Jeon (Seongnam, KR)

Access
Abstract
Slides
Like
Thumbnail

Machine learning profiling of epicardial adipose tissue using multi-omics data from ventricular biopsies

Speaker: Mr A. Mircea (Lausanne, CH)

Access
Abstract
Slides
Like
Thumbnail

Machine learning-based identification of patient clusters with distinct cardiac chronotropic responses during exercise

Speaker: Ms R. Tchala Sare (Paris, FR)

Access
Abstract
Slides
Like
Thumbnail

Access the full session

From molecules to models: AI applications in omics and biomarkers

Speakers: Mr J. Versnjak, Mr M. Ali, Doctor J. Marino, Assistant Professor K. Jeon, Mr A. Mircea...
Access
Like
Thumbnail

About the event

Image

ESC Digital & AI Summit 2025

21 November - 22 November 2025

Sessions Presentations

Congress
logo Image

ESC 365 is supported by

bristol-myers-squibb Explore sponsored resources
novartis Explore sponsored resources