Mohanad Alkhodari is a DPhil. in Medical Sciences – Cardiovascular Medicine student at the Medical Sciences Division, Radcliffe Department of Medicine (RDM). He was a biomedical engineering Research Associate (RA) at the Healthcare Engineering Innovation Center (HEIC) under the Department of Biomedical Engineering at Khalifa University (KU), United Arab Emirates (UAE) for 3 years (2019-2022). Alkhodari received his Master of Science (MS) degree (top 5-10%) in biomedical engineering from American University of Sharjah (AUS), UAE and his Bachelor of Science (BS) degree (Hons.) in electrical engineering from Abu Dhabi University (ADU), UAE in 2019 and 2017, respectively.
Alkhodari has been conducting research in several engineering areas including biomedical signal analysis, medical imaging development, and artificial intelligence (AI). His current research focuses on identifying novel ways to 1) learn the multi-dimensional disease progression landscape across heart and brain related to hypertension in the UK Biobank imaging cohort and 2) generate scores for all participants reflecting their progression from health to hypertensive disease. The ultimate goal of his research is to understand the unique patterns of hypertensive disease development associated with hypertension over life course with the help of evolutionary AI-based models.
By far, Alkhodari authored and co-authored 3 book chapters and more than 35 scientific papers in international journals and conferences, where he was the first/leading author in 28 and the corresponding author or main speaker in 17. In addition, he serves as an active reviewer for several papers in Frontiers journal (Physiology, Artificial Intelligence, Big Data, and Signal Processing). He won several awards including international PhysioNet/Computing in Cardiology Challenge 2022 (Top 4/28), regional Undergraduate Research Competition (URC) 2017 (1st/12), and national UAE Ministry of Health and Prevention (MOHAP) Innovations in Health Hackathon 2019 (1st/8).
Alkhodari spends his free time doing gym workouts. He enjoys graphic designing, drawing, as well as taking care of his two bunnies.
Deep learning identifies cardiac coupling between mother and fetus during gestation
Alkhodari M. et al, (2022), Frontiers in Cardiovascular Medicine
Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles
Alkhodari M. et al, (2021), Frontiers in Cardiovascular Medicine
Revisiting left ventricular ejection fraction levels: a circadian heart rate variability-based approach
Alkhodari M. et al, (2021), IEEE Access
Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings
Alkhodari M. and Fraiwan L., (2021), Computer Methods and Programs in Biomedicine
Predicting Hypertensive Patients With Higher Risk of Developing Vascular Events Using Heart Rate Variability and Machine Learning
Alkhodari M. et al, (2020), IEEE Access
Estimating Left Ventricle Ejection Fraction Levels Using Circadian Heart Rate Variability Features and Support Vector Regression Models
Alkhodari M. et al, (2020), IEEE journal of biomedical and health informatics