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Westerlund, A. ; Hawe, J.S.* ; Heinig, M. ; Schunkert, H.*

Risk prediction of cardiovascular events by exploration of molecular data with explainable artificial intelligence.

Int. J. Mol. Sci. 22:10291 (2021)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events). Apart from affecting the individual, this leads to high medical costs for society. Personalized treatment strategies aiming at prediction and prevention of recurrent events rely on early diagnosis and precise prognosis. Complementing the traditional environmental and clinical risk factors, multi-omics data provide a holistic view of the patient and disease progression, enabling studies to probe novel angles in risk stratification. Specifically, predictive molecular markers allow insights into regulatory networks, pathways, and mechanisms underlying disease. Moreover, artificial intelligence (AI) represents a powerful, yet adaptive, framework able to recognize complex patterns in large-scale clinical and molecular data with the potential to improve risk prediction. Here, we review the most recent advances in risk prediction of recurrent cardiovascular events, and discuss the value of molecular data and biomarkers for understanding patient risk in a systems biology context. Finally, we introduce explainable AI which may improve clinical decision systems by making predictions transparent to the medical practitioner.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Ai ; Biomarkers ; Cardiovascular Disease ; Coronary Artery Disease ; Explainable Artificial Intelligence ; Genomics ; Machine Learning ; Molecular Networks ; Multi-omics ; Proteomics; Coronary-artery-disease; Deep Neural-networks; Heart-disease; Alzheimers-disease; Recurrent Events; Vascular Events; Gene-ontology; Score; Association; Validation
Sprache englisch
Veröffentlichungsjahr 2021
HGF-Berichtsjahr 2021
ISSN (print) / ISBN 1661-6596
e-ISSN 1422-0067
Zeitschrift International Journal of Molecular Sciences
Quellenangaben Band: 22, Heft: 19, Seiten: , Artikelnummer: 10291 Supplement: ,
Verlag MDPI
Verlagsort Basel
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-553500-001
Förderungen Federal Ministry of Education and Research
Bavarian State Ministry of Health and Care
German Research Foundation (DFG)
Leducq Foundation for Cardiovascular Research
British Heart Foundation (BHF)/German Centre of Cardiovascular Research (DZHK)-collaboration
German Centre of Cardiovascular Research
German Federal Ministry of Education and Research (BMBF)
DOI 10.3390/ijms221910291
WOS ID WOS:000708177400001
Scopus ID 85115660689
PubMed ID 34638627
Erfassungsdatum 2021-11-15
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