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

Presentation Materials: