An evidential knowledge representation for drug-mechanisms and its application to drug safety

A major challenge to designers of informatics tools that help alert clinicians to potential drug-drug interactions (DDIs) is how to best assist clinicians when they must infer the potential risk of an adverse event between medication combinations that have not been studied together in a clinical trial. The central thesis of this dissertation is that DDI prediction using drug mechanism knowledge can help drug-interaction knowledge bases expand their coverage beyond what has been tested in clinical trials while avoiding prediction errors that occur when individual drug differences are not recognized. This dissertation describes a knowledge representation system, called the Drug Interaction Knowledge Base (DIKB), that uses a novel approach to linking and assessing evidence support for drug-mechanism assertions. The DIKB is the first knowledge-representation system we are aware of to use a computable model of evidence and a Truth Maintenance System to manage assertions in its knowledge-base. The novel approach to evidence management implemented in the DIKB enables its prediction accuracy and coverage to be optimized to a particular body of evidence; a feature that is very desirable for clinical decision support. The DIKB is also novel for its computable representation of the conjectures behind a specific application of evidence. These evidence-use assumptions enable the system to flag when a conjecture has become invalid and alert knowledge-base maintainers to the need to reassess their original interpretation of what assertions a piece of evidence supports. They are also used as evidence is input into the system to help identify a pattern, called a circular line of evidence support, that is indicative of fallacious reasoning by evidence-base curators. The DIKB has been shown capable of accurately predicting clinically-relevant DDIs using only pharmacokinetic drug-mechanism knowledge and development of the system has helped to identify and evaluate potential informatics solutions to the challenges of representing, synthesizing, and maintaining drug mechanism knowledge.