Modeling drug mechanism knowledge using evidence and truth maintenance.
To protect the safety of patients, it is vital that researchers find methods for representing drug mechanism knowledge that support making clinically relevant drug-drug interaction (DDI) predictions. Our research aims to identify the challenges of representing and reasoning with drug mechanism knowledge and to evaluate potential informatics solutions to these challenges through the process of developing a knowledge-based system capable of predicting clinically relevant DDIs that occur via metabolic mechanisms. In previous work, we designed a simple, rule-based, model of metabolic inhibition and induction and applied it to a database containing assertions about 267 drugs. This pilot system taught us that drug mechanism knowledge is often dynamic, missing, or uncertain. In this paper, we propose methods to address these properties of mechanism knowledge and describe a new prototype system, the Drug Interaction Knowledge-base (DIKB), that implements our proposed methods so that we can explore their strengths and limitations. A novel feature of the DIKB is its use of a truth maintenance system to link changes in the evidence support for assertions about drug properties to the set of interactions and non-interactions the system predicts.
Program of Biomedical and Health Informatics, University of Washington, Seattle, WA 98195-7240, USA . boycer@u.washington.edu
Richard D
RD
Boyce
Carol
C
Collins
John
J
Horn
Ira
I
Kalet
D001185:Artificial Intelligence; D016208:Databases, Factual; D004347:Drug Interactions; D019317:Evidence-Based Medicine; D016015:Logistic Models; D008490:Medical Informatics / Q000379:methods*; D009323:Natural Language Processing