Abstract: Clinical phenotyping provides important insight into the manifestation and outcome of rare and complex diseases. Traditional phenotyping techniques often require multiple iterations of refinement with a domain expert, lack interoperability, and have limited reproducibility. In comparison, patient similarity-based techniques derive personalized patient risk models that are highly accurate, even when applied to sparse data or poorly characterized diseases/outcomes. We introduce a novel, semi-supervised data-driven method for applying clinical similarity to pediatric phenotyping.
Learning Objective 1: Define clinical phenotyping and patient similarity approaches to phenotyping.
Learning Objective 2 (Optional): Identify limitations of current supervised or expert-driven clinical phenotyping techniques.
Tiffany Callahan (Presenter)
University of Colorado Denver
Olivier Bodenreider, National Library of Medicine, National Institutes of Health
Michael Kahn, University of Colorado Denver Anschutz Medical Campus