Muhammad Mamdani

MPH, MA, PharmD

Scientist

Biography

Dr. Mamdani is Vice President of Data Science and Advanced Analytics at Unity Health Toronto and Director of the University of Toronto Temerty Faculty of Medicine Centre for Artificial Intelligence Education and Research in Medicine (T-CAIREM). Dr. Mamdani’s team bridges advanced analytics including machine learning with clinical and management decision making to improve patient outcomes and hospital efficiency. Dr. Mamdani is also Professor in the Department of Medicine of the Temerty Faculty of Medicine, the Leslie Dan Faculty of Pharmacy, and the Institute of Health Policy, Management and Evaluation of the Dalla Lana Faculty of Public Health. He is also adjunct Senior Scientist at the Institute for Clinical Evaluative Sciences (ICES) and a Faculty Affiliate of the Vector Institute, which is a leading institution for artificial intelligence research in Canada.

Dr. Mamdani holds a Doctor of Pharmacy degree from the University of Michigan, a fellowship in pharmacoeconomics from the Detroit Medical Centre, a Master of Arts degree in econometric theory from Wayne State University, and a Master of Public Health from Harvard University with a focus on statistics and epidemiology. He has previously been named among Canada’s Top 40 under 40. Dr. Mamdani’s research interests include pharmacoepidemiology, pharmacoeconomics, drug policy, and the application of advanced analytics approaches to clinical problems and health policy decision-making. He has published over 500 studies in peer-reviewed healthcare journals.

Please note: Dr. Mamdani is not taking any summer students

Recent Publications

  1. Li, B, Nassereldine, R, Zamzam, A, Syed, MH, Mamdani, M, Al-Omran, M et al.. Development and evaluation of a prediction model for peripheral artery disease related major adverse limb events using novel biomarker data. J Vasc Surg. 2024; :. doi: 10.1016/j.jvs.2024.03.450. PubMed PMID:38599293 .
  2. Li, B, Warren, BE, Eisenberg, N, Beaton, D, Lee, DS, Aljabri, B et al.. Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD. JAMA Netw Open. 2024;7 (3):e242350. doi: 10.1001/jamanetworkopen.2024.2350. PubMed PMID:38483388 PubMed Central PMC10940965.
  3. Fitzgibbon, JJ, Heindel, P, Appah-Sampong, A, Holden-Wingate, C, Hentschel, DM, Mamdani, M et al.. Temporal trends in hemodialysis access creation during the fistula first era. J Vasc Surg. 2024; :. doi: 10.1016/j.jvs.2024.02.020. PubMed PMID:38387816 .
  4. Li, B, Verma, R, Beaton, D, Tamim, H, Hussain, MA, Hoballah, JJ et al.. Predicting outcomes following lower extremity open revascularization using machine learning. Sci Rep. 2024;14 (1):2899. doi: 10.1038/s41598-024-52944-1. PubMed PMID:38316811 PubMed Central PMC10844206.
  5. Smith, CW, Malhotra, AK, Hammill, C, Beaton, D, Harrington, EM, He, Y et al.. Vision Transformer-based Decision Support for Neurosurgical Intervention in Acute Traumatic Brain Injury: Automated Surgical Intervention Support Tool. Radiol Artif Intell. 2024;6 (2):e230088. doi: 10.1148/ryai.230088. PubMed PMID:38197796 PubMed Central PMC10982820.
  6. Antoniou, T, Pajer, K, Gardner, W, Penner, M, Lunsky, Y, McCormack, D et al.. Impact of COVID-19 pandemic on prescription stimulant use among children and youth: a population-based study. Eur Child Adolesc Psychiatry. 2024; :. doi: 10.1007/s00787-023-02346-x. PubMed PMID:38180538 .
  7. Antoniou, T, Pajer, K, Gardner, W, Penner, M, Lunsky, Y, Tadrous, M et al.. Impact of the COVID-19 pandemic on antidepressant and antipsychotic use among children and adolescents: a population-based study. Front Pediatr. 2023;11 :1282845. doi: 10.3389/fped.2023.1282845. PubMed PMID:38146536 PubMed Central PMC10749316.
  8. Heindel, P, Dey, T, Fitzgibbon, JJ, Mamdani, M, Hentschel, DM, Belkin, M et al.. Predicting recurrent interventions after radiocephalic arteriovenous fistula creation with machine learning and the PREDICT-AVF web app. J Vasc Access. 2023; :11297298231203356. doi: 10.1177/11297298231203356. PubMed PMID:38143431 .
  9. Li, B, Eisenberg, N, Beaton, D, Lee, DS, Aljabri, B, Verma, R et al.. Using Machine Learning (XGBoost) to Predict Outcomes After Infrainguinal Bypass for Peripheral Artery Disease. Ann Surg. 2024;279 (4):705-713. doi: 10.1097/SLA.0000000000006181. PubMed PMID:38116648 .
  10. Dryden, L, Song, J, Valenzano, TJ, Yang, Z, Debnath, M, Lin, R et al.. Evaluation of Machine Learning Approaches for Predicting Warfarin Discharge Dose in Cardiac Surgery Patients: Retrospective Algorithm Development and Validation Study. JMIR Cardio. 2023;7 :e47262. doi: 10.2196/47262. PubMed PMID:38055310 PubMed Central PMC10733832.
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Affiliations & Other Activities

  • Scientist, Li Ka Shing Knowledge Institute, St. Michael’s Hospital
  • Professor, Institute of Health Policy, Management, and Evaluation, University of Toronto
  • Professor, Leslie Dan Faculty of Pharmacy, University of Toronto
  • Adjunct Professor, King Saud University Senior Adjunct
  • Scientist, Institute for Clinical Evaluative Sciences