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Interpretable machine learning-derived risk score for significant coronary artery calcification in a real-world clinical cohort

29 August 2026 (17:15 - 18:00)
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Congress Presentation Part of: Predicting cardio-renal-metabolic disease (1) Scores ESC2026 Premium Access ESC Congress 2026 European Society of Cardiology

About the speaker

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Professor Jae-Sik Jang

Dongguk University International Hospital, Goyang (Korea (Republic of))

6 More presentations in this session

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Machine learning-based all-cause mortality risk assessment integrating novel biomarkers in patients with suspected or known coronary artery disease: the AI-ANOX-EXCEED-J Study
Doctor H. Wada (Kyoto, JP)
Abstract Slides Video
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Serum Let-7b and AKT3 predict cardiovascular death in type 2 diabetes: insights from miRNA profiling and target prediction
Miss H. Evirgen (Ankara, TR)
Abstract Slides Video
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Combined assessment of stress hyperglycemia ratio and hemoglobin glycation index to predict mortality after coronary revascularisation: a machine learning study
Miss L. Runqian (CN)
Abstract Slides Video
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Relationship between atherosclerotic burden and subclinical cardiac dysfunction in Type 2 Diabetes
Doctor T. Jinadu (Leicester, GB)
Abstract Slides Video
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Fourteen-year risk of MACE stratified by NT-proBNP and inflammatory biomarkers in type 2 diabetes
Doctor H. Bahrami (Herlev, DK)
Abstract Slides Video

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Predicting cardio-renal-metabolic disease (1)

The Event

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ESC Congress 2026

29 August 2026 17:15 CET
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