Manaf Zargoush
Additional links
Overview
Manaf Zargoush is an associate professor of Health Policy and Management at the DeGroote School of Business. His area of expertise lies in analyzing large electronic health records with data science (descriptive, predictive, and prescriptive analytics) for decision planning and policymaking in the healthcare system. Currently, he is working on various projects that utilize artificial intelligence and big data to manage ALC (Alternate Level of Care) and LTC bed allocations, understand disability transitions among older adults, and management of chronic diseases such as hypertension and diabetes. Manaf is passionate about data science, healthcare management, optimization, and age-related healthcare.
Affiliations
- Associate Professor
- DeGroote School of Business, McMaster University
- Member, McMaster Institute for Research on Aging (MIRA), McMaster University
- Associate Professor, Health Policy and Management, DeGroote School of Business
- Member, McMaster Digital Transformation Research Centre, DeGroote School of Business
Education
- BSc (Mechanical Engineering), Jundi-Shapoor University, Iran
- MPhil (Decision Science and Statistics), ESSEC Business School, France
- MSc (Industrial Engineering), Sharif University of Technology, Iran
- PhD (Healthcare Operations and Information Management), McGill University, Canada
- PhD (Decision Science and Statistics), ESSEC Business School, France
Related Projects
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Relationship between biomarkers and musculoskeletal pain and falls in older adults – A cross-sectional analysis of the Canadian Longitudinal Study on Aging data
Diego Roger Silva | 2021 | MIRA Postdoctoral Fellow
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Identification of older adults’ mobility decline trajectories in longitudinal studies using machine learning approaches
Mina Nourdanesh | 2021 | MIRA Postdoctoral Fellow
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Building on what we know: Using design thinking to tailor routine assessments and interventions for fall and fracture prevention in long-term care
Isabel Rodigues | 2021 | Labarge Postdoctoral Fellow in Mobility

