Projektbeschreibung
Computergestützte Biomarker für das Myokardinfarktrisiko
Der Myokardinfarkt ist weltweit eine der häufigsten Todesursachen, doch die derzeitigen Prognoseinstrumente für das Ischämie- und Blutungsrisiko nach einer Therapie sind nur begrenzt präzise. Das vom Europäischen Forschungsrat finanzierte Projekt ORACLE zielt darauf ab, die Risikovorhersage durch die Nutzung multidimensionaler Daten von Wearables, Biomarkern und nicht-invasiver Bildgebung zu verbessern. Die Idee besteht darin, mithilfe von KI bei der Analyse von Daten aus einer großen Kohorte von Menschen mit Myokardinfarkt neue computergestützte Biomarker für das Risiko zu ermitteln. Im Rahmen des Projekts sollen klinisch geführte KI-Algorithmen entwickelt und für fundierte Behandlungsentscheidungen in die Praxis integriert werden.
Ziel
Myocardial infarction (MI) is a leading cause of death worldwide. After MI, long-term antithrombotic therapy is crucial to prevent recurrent events, but increases bleeding, that also impacts morbidity and mortality. Giving these competing risks prediction tools to forecast ischemic and bleeding are of paramount importance to inform clinical decisions, but their current precision is limited. Improve events prediction, by discovering novel and innovative markers of risk would have a tremendous impact on therapeutic decisions and patients’ outcome. I hypothesize that using innovative multidimensional information from wearable devices, biomarkers, behavioral patterns and non-invasive imaging, integrated through artificial intelligence computation, we may discover novel “computational biomarkers” of risk and improve current standards of risk prediction. In this project, I will enroll a large cohort of MI patients, whereby prospective collection of consolidated and innovative potential risk predictors will take place, in order to generate a comprehensive and multidimensional dataset. I will collect data from state-of-the-art non-invasive imaging, blood biomarkers, wearable medical devices of continuous heart electrical activity, sweat, mobility and behavioral patterns to create a large physiological time series allowing patients’ deep phenotyping. We will therefore analyze data leveraging artificial intelligence computation to find relevant associations with clinical outcomes, and compare new algorithms with current risk prediction tools. This research will increase our knowledge on bleeding and ischemic risk factors, enabling enhanced capability predictions models. In the near future, we hypothesize that our clinically-guided Artificial Intelligence algorithm might be integrated in clinical practice, helping clinicians to inform treatment decisions, patients to better understand their risk profile, finally setting a common ground for shared patient/physician decisions.
Wissenschaftliches Gebiet
Schlüsselbegriffe
Programm/Programme
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Thema/Themen
Finanzierungsplan
HORIZON-ERC - HORIZON ERC GrantsGastgebende Einrichtung
29590 MALAGA
Spanien