I have extensive and broad clinical training in all aspects of gastroenterology, hepatology and nutrition, and advance subspecialty training in Inflammatory Bowel Disease; my clinical passion. I have trained in numerous expert leading institutes across the world: Kings College Hospital, London, Sydney Children’s Hospital and the Hospital for Sick Children, Toronto.
The primary focus of my research program is to employ predictive analytics and machine learning approaches to study the clinical and biological determinants of outcomes in Inflammatory Bowel Disease (IBD), an inherently complex and heterogeneous disease. One of our goals is to develop a robust machine learning analytical platform to further advance precision medicine in digestive diseases. With our collaborators we are currently leveraging state-of-the-art machine learning approaches to build cell classifier tool and employing deep learning approaches to derive novel disease predictive histologic signatures. We would envisage developing accurate predictive tools reflective of the population we treat, to inform clinical decision making. Furthermore, another focus of our program is to utilize the data infrastructure of the ImproveCareNow Network as a foundation to understand the social determinants of health as a means of closing gaps in outcomes of IBD care.
Previous research projects have focused on defining the heterogeneity of pediatric IBD, classifying the type of IBD, and most recently exploring the ethno-racial differences in pediatric IBD.
BSc: Pharmacology and Physiology, University College of London, UK.
MBBS: Guy’s Kings and St Thomas, University of London, UK.
Pediatric Specialist Training in Pediatric Gastroenterology, Hepatology and Nutrition (CCT): National Health Service, Pediatric London Program, London, UK.
Clinical Fellowship: Hospital for Sick Children, Toronto, ON, Canada.
Advance Inflammatory Bowel Disease Fellowship: Hospital for Sick Children, Toronto, ON, Canada.
MSc: Clinical Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Certification: Membership of the Royal College of Paediatrics and Child Health.
Pediatric gastroenterology; pediatric inflammatory bowel disease
Artificial intelligence; deep learning; machine learning; predictive analytics; clinical decision support tool and social determinants of health
Biomedical Informatics, Gastroenterology Hepatology and Nutrition
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The Phenotypic Spectrum of New-onset IBD in Canadian Children of South Asian Ethnicity: A Prospective Multi-Centre Comparative Study. Journal of Crohn's and Colitis. 2022; 16:216-223.
Incidence of Inflammatory Bowel Disease in South Asian and Chinese People: A Population-Based Cohort Study from Ontario, Canada. Clinical Epidemiology. 2021; 13:1109-1118.
One-year outcomes with ustekinumab therapy in infliximab-refractory paediatric ulcerative colitis: a multicentre prospective study. Alimentary Pharmacology and Therapeutics. 2021; 53:1300-1308.
Accurate Classification of Pediatric Colonic Inflammatory Bowel Disease Subtype Using a Random Forest Machine Learning Classifier. Journal of Pediatric Gastroenterology and Nutrition. 2021; 72:262-269.
Phenotypic Variation in Paediatric Inflammatory Bowel Disease by Age: A Multicentre Prospective Inception Cohort Study of the Canadian Children IBD Network. Journal of Crohn's and Colitis. 2020; 14:445-454.
The incidence of IBD among South Asian children is rising - but are the phenotype and outcomes distinct?. Journal of Pediatric Gastroenterology and Nutrition. 2023.
DEVELOPMENT OF AN OPTIMAL MACHINE LEARNING MODEL USING TREATMENT NAÏVE DIAGNOSTIC PATHOLOGY IMAGES TO PREDICT STEROID-FREE CLINICAL REMISSION AT ONE YEAR IN PEDIATRIC ULCERATIVE COLITIS. Inflammatory Bowel Diseases. 2023; 29:s56-s57.
Predicting Therapeutic Response in Pediatric Ulcerative Colitis-A Journey Towards Precision Medicine. Frontiers in Pediatrics. 2021; 9.
Body Composition Using Air Displacement Plethysmography in Children With Inflammatory Bowel Disease. Journal of Pediatric Gastroenterology and Nutrition. 2020; 71:52-58.
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