Computing with evidence

  • Authors:

  • Richard Boyce;Carol Collins;John Horn;Ira Kalet

  • Affiliations:

  • Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Avenue, VALE M-183, Pittsburgh, PA 15260, USA;School of Pharmacy, University of Washington, Seattle, WA, USA;School of Pharmacy, University of Washington, Seattle, WA, USA;Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA, USA

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2009

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Abstract

We describe a novel experiment that we conducted with the Drug Interaction Knowledge-base (DIKB) to determine which combinations of evidence enable a rule-based theory of metabolic drug-drug interactions to make the most optimal set of predictions. The focus of the experiment was a group of 16 drugs including six members of the HMG-CoA-reductase inhibitor family (statins). The experiment helped identify evidence-use strategies that enabled the DIKB to predict significantly more interactions present in a validation set than the most rigorous strategy developed by drug experts with no loss of accuracy. The best-performing strategies included evidence types that would normally be of lesser predictive value but that are often more accessible than more rigorous types. Our experimental methods represent a new approach to leveraging the available scientific evidence within a domain where important evidence is often missing or of questionable value for supporting important assertions.