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Cost-aware prediction (CAP): an LLM-supported machine learning pipeline for interpreting heart failure mortality predictions

21 November 2025 (13:10 - 13:55)
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Congress Presentation Part of: From bench to bedside: the potential roles of large language models in cardiovascular medicine Hospital Information Systems, Electronic Medical Records, Clinical Decision Support, Other Open Access ESC Digital & AI Summit 2025 European Society of Cardiology

About the speaker

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Assistant Professor Yinan Yu

Chalmers University of Technology, Gothenburg (Sweden)

6 More presentations in this session

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Clinical performance and readability evaluation of large language models for patient communication in heart failure and cardiomyopathies
Doctor C. Reich (Heidelberg, DE)
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Large language model (LLM)-based agentic artificial intelligence tool streamlines research processes in biomarker studies: a proof of concept
Ms Y. Ye (Copenhagen, DK)
Abstract Slides
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DigiLearnHF - an LLM-enhanced digital learning program for patients with implantable defibrillators and heart failure
Ms A. Sluha (Hannover, DE)
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Assessing the reliability of large language models for reviewing AI research in cardiac electrophysiology using the EHRA AI in EP checklist
Ms A. Sluha (Hannover, DE)
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CARMINA: optimizing low-parameter language models for high-quality cardiovascular research assistance
Mr J. Alvarez-Arenas (Madrid, ES)
Abstract Slides

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From bench to bedside: the potential roles of large language models in cardiovascular medicine

The Event

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ESC Digital & AI Summit 2025

21 November 2025 13:10 CET

ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources