Diabetic retinopathy is the leading cause of blindness in working age adults in the United States. It is challenging to address in both rural and urban underserved settings, which suffer from shortages of eye specialists. This talk will describe the approach taken to address this condition in a medically underserved area (South Los Angeles) by researchers in the Center for Biomedical Informatics at Charles R. Drew University of Medicine and Science, using telehealth and machine learning on data from patient electronic health records.
Omolola Ogunyemi, PhD, FACMI is the Director of Charles R. Drew University of Medicine and Science's Center for Biomedical Informatics and a co-chair of the UCLA CTSI's biomedical informatics program. She is also an Adjunct Professor of Radiological Sciences in the David Geffen School of Medicine at UCLA with the Medical and Imaging Informatics group. She was recently a Principal Investigator on a National Library of Medicine (NLM)-funded R01 grant to develop a variety of machine learning approaches for identifying patients with latent/undiagnosed diabetic retinopathy from electronic health records or digital retinal images. Dr. Ogunyemi’s research at the CBI focuses on novel biomedical informatics solutions for problems that affect medically underserved communities. Her research interests include computerized medical decision support, reasoning under uncertainty, 3D graphics and visualization, and machine learning. Prior to her role at Charles R. Drew University of Medicine and Science, Dr. Ogunyemi was a biomedical informatics faculty member in the Department of Radiology at Brigham and Women's Hospital and Harvard Medical School. She was also a member of the affiliated faculty in the Harvard-MIT Division of Health Sciences and Technology. Dr. Ogunyemi holds an undergraduate degree in Computer Science from Barnard College, Columbia University and an M.S.E, and Ph.D. in Computer and Information Science from the University of Pennsylvania.