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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About the speaker

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)

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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)

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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)

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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

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