Computing with evidence Part I: A drug-mechanism evidence taxonomy oriented toward confidence assignment.
We present a new evidence taxonomy that, when combined with a set of inclusion criteria, enable drug experts to specify what their confidence in a drug mechanism assertion would be if it were supported by a specific set of evidence. We discuss our experience applying the taxonomy to representing drug-mechanism evidence for 16 active pharmaceutical ingredients including six members of the HMG-CoA-reductase inhibitor family (statins). All evidence was collected and entered into the Drug-Interaction Knowledge Base (DIKB); a system that can provide customized views of a body of drug-mechanism knowledge to users who do not agree about the inferential value of particular evidence types. We provide specific examples of how the DIKB's evidence model can flag when a particular use of evidence should be re-evaluated because its related conjectures are no longer valid. We also present the algorithm that the DIKB uses to identify patterns of evidence support that are indicative of fallacious reasoning by the evidence-base curators.
Department of Biomedical Informatics, University of Pittsburgh, VALE M, PA 15260, USA. rdb20@pitt.edu
Richard
R
Boyce
Carol
C
Collins
John
J
Horn
Ira
I
Kalet
D000465:Algorithms; D016001:Confidence Intervals; D016208:Databases, Factual; D004347:Drug Interactions; D019317:Evidence-Based Medicine; D006801:Humans; D008490:Medical Informatics; D004364:Pharmaceutical Preparations / Q000592:standards*; D010599:Pharmacokinetics; D009626:Terminology as Topic
D004364:Pharmaceutical Preparations