Dr. Khaled El Emam is a Senior Scientist at the Children’s Hospital of Eastern Ontario Research Institute and the Canada Research Chair (Tier 1) in Medical AI at the University of Ottawa. Khaled heads 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 School of Epidemiology and Public Health, 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.
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Research Projects
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Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study
03/03/2025
Recruiting a sufficient number of patients for clinical trials is challenging [1], and the inability to recruit participants is the cause of failure for many clinical trials [2]. Approximately, 25% of clinical trials are discontinued before completion [3], with insufficient recruitment being the most frequent reason in 31% of the cases [4]. For adult cancer trials, between 20% and 50% fail to complete or were unable to reach recruitment goals [5-9]. This has been exacerbated by the recent pandemic where many trials experienced a considerable reduction in recruitment rates [10-13], which has continued after the pandemic [12]. While poor accrual is a problem in all trials, it is a greater problem in government (ie, academic) sponsored trials [14,15]. When a study is unable to recruit a sufficient number of patients, the study can be stopped, and the relevant analyses are performed on the available data. However, not reaching accrual targets results in underpowered analyses, and the smaller sample sizes increase the risk of unstable parameter estimates.
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Advancing data science in drug development through an innovative computational framework for data sharing and statistical analysis
14/11/2021
Establishing this framework has been integral to the development of analytical tools.
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Role of Sex and Gender in Access to Care and Cardiovascular Complications of Individuals with Diabetes Mellitus
01/10/2021
Country-specific gender related factors and gender disparity must be targeted for improving health status and access to care of patients with DM.
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Can synthetic data be a proxy for real clinical trial data? A validation study
16/04/2021
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.
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Identification and inclusion of gender factors in retrospective cohort studies: the GOING-FWD framework
09/04/2021
The application of the GOING-FWD multistep approach can help guide investigators to analyse gender and its impact on outcomes in previously collected data.