{"id":1227,"date":"2022-10-14T20:42:09","date_gmt":"2022-10-14T20:42:09","guid":{"rendered":"https:\/\/bcchrmicro.wpengine.com\/?page_id=2"},"modified":"2026-06-18T20:47:47","modified_gmt":"2026-06-18T20:47:47","slug":"home","status":"publish","type":"page","link":"https:\/\/www.bcchr.ca\/adsu\/","title":{"rendered":"Home"},"content":{"rendered":"
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AI and Data Science Unit<\/h1>

At the AI and Data Science Unit (ADSU), we believe in leveraging the power of technology and innovation to improve pediatric care.<\/p>

ADSU aims to build institutional capacity for the ethical, evidence-based translation of AI innovations into clinical practice, fostering improved outcomes and lasting impact across research and health care environments.<\/p>Projects<\/a><\/div>\n

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Recent advances in artificial intelligence (AI) and data science hold significant promise for transforming pediatric research, enhancing clinical care, and improving experiences for families and providers. However, barriers such as limited technical capacity, infrastructure, and governance constrain their effective application at BCCH(R). The Artificial Intelligence and Data Science Unit (ADSU), an initiative stemming from Integrated Research Informatics Services (IRIS), addresses this gap by offering specialized expertise and advanced tools. ADSU aims to build institutional capacity for the ethical, evidence-based translation of AI innovations into clinical practice, fostering improved outcomes and lasting impact across research and health care environments.<\/p>

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Projects<\/h2>
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Pediatric Epilepsy: AI-Assisted Surgical Decision Support<\/h3> <\/span>\n\t\t\t \n \n <\/svg>\n\t\t\t <\/button>\n \n
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Led by Dr. Mandeep Tamber (Neurosurgeon, BCCH), the broader aim of this project is to improve both patient selection and surgical decision-making in pediatric epilepsy, using AI assisted methods.<\/p>

To do this, a multimodal approach combining low- and high-dimensional pre-surgical data to predict seizure freedom at 1-year postoperatively is being tried. As part of this, we validated using LLMs to automatically extract clinically relevant features from MRI reports that is then feed into the predictive model \u2014 helping clinicians identify the right candidates for surgery and anticipate results.<\/p>

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B. King presenting a poster at Canadian League Against Epilepsy 2025 ASM at Calgary<\/em><\/p><\/div>\n

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V. Bahel presenting a poster at American Association of Neurosurgeons 2025 ASM at Boston<\/em><\/p><\/div>\n<\/div><\/div>\n\n

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Posters\/Papers:<\/strong><\/p>\n

PO1- Feature extraction from MRI clinical reports in Pediatric Epilepsy: Rule-based, Open-weight LLMs and Hybrid Methods.<\/strong><\/em><\/em><\/summary>

King B, Bahel VR, Paul S, Abraham AP, Kim D, G\u00f6rges M, Bjornson BH, Sujan J, van Rooij T, Tamber MS. In: Proceedings of CLAE 2025 Annual Scientific Meeting. Calgary, AB: Canadian League Against Epilepsy; 2025.<\/em><\/p>\n\n

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