Dr. Khaled El Emam is a Senior Scientist at the Children’s Hospital of Eastern Ontario Research Institute and head of the multi-disciplinary Electronic Health Information Laboratory, conducting research on privacy enhancing technologies to enable the sharing of health data for secondary purposes, including de-identification methods and synthetic data generation. He is also a Professor in the Faculty of Medicine at the University of Ottawa.
As an entrepreneur, Khaled founded or co-founded six companies involved with data management and data analytics. In 2003 and 2004, he was ranked as the top systems and software engineering scholar worldwide by the Journal of Systems and Software based on his research on measurement and quality evaluation and improvement.
Previously, Khaled was a Senior Research Officer at the National Research Council of Canada. He also served as the head of the Quantitative Methods Group at the Fraunhofer Institute in Kaiserslautern, Germany.
Khaled held the Canada Research Chair in Electronic Health Information at the University of Ottawa from 2005 to 2015. He has a PhD from the Department of Electrical and Electronics Engineering, King’s College, at the University of London, England.
Advancing data science in drug development through an innovative computational framework for data sharing and statistical analysis
Establishing this framework has been integral to the development of analytical tools.
Role of Sex and Gender in Access to Care and Cardiovascular Complications of Individuals with Diabetes Mellitus
Country-specific gender related factors and gender disparity must be targeted for improving health status and access to care of patients with DM.
Can synthetic data be a proxy for real clinical trial data? A validation study
The high concordance between the analytical results and conclusions from synthetic and real data suggests that synthetic data can be used as a reasonable proxy for real clinical trial datasets.
Identification and inclusion of gender factors in retrospective cohort studies: the GOING-FWD framework
The application of the GOING-FWD multistep approach can help guide investigators to analyse gender and its impact on outcomes in previously collected data.
Evaluating the utility of synthetic COVID-19 case data
A privacy risk assessment on the synthetic data showed that the attribute and membership disclosure risks were low.