Department of Health Informatics and Data Science and 

Center for Health Outcome and Informatics Research



“ECG-AI: Electrocardiographic Artificial Intelligence Model for Prediction of Heart Failure”


  Presented by:


Ibrahim Karabayir, PhD

Research Associate, Department of Health Informatics and Data Sciene, Loyola University Chicago


Abstract: Heart failure (HF) is one of the leading causes of death in the US. Early diagnosis and treatment can prevent adverse health outcome and economic burdens of HF. A review of the literature shows there is a critical need for non-invasive approach using widely available tools for screening patients with risks of HF, and its sub-types such as Preserved left ventricular Ejection Fraction (HFpEF) and Reduced left ventricular Ejection Fraction (HFrEF). Dr. Karabayir will present how electrocardiogram (ECG) and other clinical factors can be used by traditional machine learning and deep learning algorithms to help evaluate the risk of HF and its subtypes. Furthermore, he will discuss about uncovering black-box side of artificial intelligence to understand ECG markers of HF risk by using GRAD-CAM algorithm.


When: Wednesday, JuLY 28th        11:00 am – 12:00 pm

Join via Zoom: https://luc.zoom.us/j/88067078491


About the Speaker: Dr. Karabayir completed his PhD, in which he proposed a novel learning algorithm for deep convolutional network, in 2019 in the field of AI from Istanbul University, Turkey. His research focus is developing and modifying deep learning and machine learning algorithms and their applications in different clinical settings. He is currently Postdoctoral Research Associate under the supervising of Dr. Oguz Akbilgic at Loyola University Chicago. He maintains different project using artificial intelligence based algorithms such as assessing risk of heart failure, COVID-19 infections, development of acute respiratory distress syndrome, rapid decline of kidney function in sickle cell anemia, late-onset cardiomyopathy, and Parkinson’s disease.


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