ECG-AI: Electrocardiographic Artifical Intelligence Model for Prediction of Heart Failure
Overview:
Ibrahim Karabayir, PhD presents how electrocardiogram (ECG) and other clinical factors can be used by traditional machine learning and deep learning algorithms to help evaluate the risk of Heart failure (HF) and its subtypes. Furthermore, he discusses uncovering black-box side of artificial intelligence to understand ECG markers of HF risk by using GRAD-CAM algorithm.
Speaker:
Ibrahim 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 a postdoctoral research associate under the supervision of Oguz Akbilgic at Loyola University Chicago. He maintains different projects 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.
Originally recorded on Wednesday, July 28, 2021, as part of CHOIR's
Overview:
Ibrahim Karabayir, PhD presents how electrocardiogram (ECG) and other clinical factors can be used by traditional machine learning and deep learning algorithms to help evaluate the risk of Heart failure (HF) and its subtypes. Furthermore, he discusses uncovering black-box side of artificial intelligence to understand ECG markers of HF risk by using GRAD-CAM algorithm.
Speaker:
Ibrahim 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 a postdoctoral research associate under the supervision of Oguz Akbilgic at Loyola University Chicago. He maintains different projects 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.