- Rush Medical College, Chicago, Illinois – MD
- Rush University Medical Center – Residency in Internal Medicine
- University of Maryland Medical Center, Baltimore, Maryland – Fellowship in Pulmonary and Critical Care Medicine
- Rush University – Research Methods and Epidemiology, MS
Dr. Afshar is a faculty member in the Division of Allergy, Pulmonary and Critical Care Medicine within the Department of Medicine. He is a steering committee member of the American Medical Informatics Association Clinical Decision Support working group and Vice-Chair for the 2021 Informatics Summit. He has served on study sections for the Department of Defense, National Institute of Health, Biomedical Computing and Health Informatics, and the Agency for Healthcare Research and Quality. In 2013, he was a recipient of a National Institutes of Health training grant, the Ruth L. Kirschstein Institutional National Research Service Award, that prepares individuals to have a significant impact on the health-related research needs of the Nation. In 2016, Dr. Afshar received a Career Development Award from the NIH for his focus on patient-oriented research. He is a member of a number of professional societies, including as a committee member for the American Thoracic Society and the Shock Society.
Dr. Afshar’s clinical specialties include clinical informatics, critical care medicine, and pulmonary medicine.
View Dr. Majid Afshar's publications on NCBI MyBibliography
Dr. Afshar’s research interests include prediction and prognostication using electronic health record data and public health data for individuals with substance use disorders, and critically ill patients with respiratory failure. His focus is on early detection and monitoring using methods in natural language processing and machine learning. He has previously developed models for identifying substance misuse and respiratory failure in hospitalized patients, as well as a risk prediction model for acute respiratory distress syndrome in burn patients. He has helped multiple institutions develop large-scale NLP pipelines for big data research needs.