Prof. Mohamed Abdolell joined the Dept. of Diagnostic Radiology, Dalhousie University, July 2005. He holds an appointment at the rank of Associate Professor in the Dept. of Diagnostic Radiology & Division of Medical Education/Informatics, Dalhousie University, and holds the rank of Affiliated Scientist with the QEII Health Sciences Centre. Mohamed is an accredited Professional Statistician with the Statistical Society of Canada.
He earned his MSc in Biostatistics from the Department of Public Health Sciences, Faculty of Medicine, University of Toronto in 1995 under the supervision of Prof. Michael LeBlanc. His MSc thesis entitled, "Poisson Regression Trees", focused on extending CART methodology to the context of Poisson distributed outcomes.
He earned his MSc in Biostatistics from the Department of Public Health Sciences, Faculty of Medicine, University of Toronto in 1995 under the supervision of Prof. Michael LeBlanc. His MSc thesis entitled, "Poisson Regression Trees", focused on extending CART methodology to the context of Poisson distributed outcomes.
His current research interests include: [1] developing a web-based research methods curriculum for medical residents that fills the educational void in research methods training that typically exists for medical students, residents and fellows who are not necessarily on the Clinical Epidemiology graduate studies track - the course is targeted at both those interested in research careers as well as those focussed on clinical practice but who never-the-less require sound research methodology training to enable proficiency in critical review of the medical literature for sound evidence based medical practice; [2] developing more accurate diagnostic classifiers using modern data mining algorithms incorporating both diagnostic image features and clinical information; [3] public health informatics; [4] syndromic surveillance; [5] open source EMRs; [6] point of care, programmatic and population level decision tools employing SPC and statistical learning algorithms; [7] reproducible research adhering to literate statistical programming principles.