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

21 November 2025 (08:00 - 18:00)
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Congress Presentation Part of: From molecules to models: AI applications in omics and biomarkers Machine Learning Open Access ESC Digital & AI Summit 2025 European Society of Cardiology

About the speaker

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Mr Mohamed Medhat Mohamed Ali

Ludwig Maximilians University, Munich (Germany)

5 More presentations in this session

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Leveraging structural equation modeling and machine learning on large-scale metabolomic data to uncover atrial fibrillation risks
Mr J. Versnjak (Berlin, DE)
Abstract Slides
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Machine learning reveals metabolic and inflammatory predictors of exercise adaptation in HFpEF
Doctor J. Marino (Greifswald, DE)
Abstract Slides
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AI-enhanced risk prediction in acute heart failure: combining radiographic biomarkers with clinical data
Assistant Professor K. Jeon (Seongnam, KR)
Abstract Slides
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Machine learning profiling of epicardial adipose tissue using multi-omics data from ventricular biopsies
Mr A. Mircea (Lausanne, CH)
Abstract Slides
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Machine learning-based identification of patient clusters with distinct cardiac chronotropic responses during exercise
Ms R. Tchala Sare (Paris, FR)
Abstract Slides

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From molecules to models: AI applications in omics and biomarkers

The Event

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ESC Digital & AI Summit 2025

21 November - 22 November 2025
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ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources