David Koff

McMaster University
Project Title: Personalized predictive risk for medical imaging radiation exposure
Industry Partner: Real Time Medical
Project Partners: Thomas Doyle & Reza Samavi
Platform: 
Cloud Analytics

Digital Media Health

This project will build the expert team and create the tools required to understand the long-term effects of low dose radiation exposure from medical imaging on populations and facilitate the adoption of best practices to decrease the impact of imaging related radiation exposure. Although this project will focus on low dose radiation exposure from medical imaging, the tools and approaches that will be developed will be transferrable to other specialties in the health care field. To achieve these goals, we propose to: (1) Develop a standard-based, extensible data model for a provincial platform to reconcile radiation dose with patient medical information; (2) Develop generic data mining tools that can be customized to query Big Data repositories for use with major modalities, including Electronic Patient Records and Electronic Medical Records across the province; (3) Investigate algorithmic approaches to extract and structure data from reports and text in DICOM headers, and from free text in notes and summaries in electronic patient records; (4) Develop intelligent terminology mapping and quality assurance mechanisms to ensure data quality; and, (5) Develop a feedback mechanism for decision support at the point of care related to imaging procedures.