Neurodevelopmental conditions, including autism and attention-deficit/hyperactivity disorder (ADHD), can be associated with significant distress and disability in children. Neurodevelopmental conditions are the largest identifiable subpopulation of children with disabilities and account for up to 14% of all children. The diagnostic labels of autism and ADHD in particular are broad and often lack precision to guide care. This creates a unique challenge when prescribing medications, which can significantly improve outcomes, as there is a lack of markers to guide individualized medication choice. This often results in clinicians prescribing neurodivergent children medications through a trial and error approach. The Bloorview Research Institute is housed in Holland Bloorview Kids Rehabilitation Hospital, Canada’s largest pediatric rehabilitation hospital recognized globally for its unique client population and leadership in childhood care.
The Bloorview Research Institute wanted to explore innovative ways to pilot the analysis of demographic, phenotypic, and medication data from the POND Network. They wanted a cloud-based solution that can analyse datasets from collaborating hospitals and universities with the goal of federating insights on biases in medication prescription patterns based on demographic identities, as well applying machine learning techniques to predict medication use in children with neurodevelopmental conditions.
The Bloorview Research Institute leveraged Omics AI to connect disparate datasets from POND partners and analyse it through Workbench. The solution uses Publisher to connect data and Workbench to process it. Machine learning services were also used to support the creation of novel machine learning algorithms.