Design Recommendations for Pharmacogenomics Clinical Decision Support Systems

Creating Shareable Clinical Decision Support Rules for a Pharmacogenomics Clinical Guideline Using Structured Knowledge Representation

Pharmacogenomics (PGx) guidelines contain drug-gene relationships, therapeutic and clinical recommendations from which clinical decision support (CDS) rules can be extracted, rendered and then delivered through clinical decision support systems (CDSS) to provide clinicians with just-in-time information at the point of care. Several tools exist that can be used to generate CDS rules that are bas...

Clinical Pharmacogenomics: Premarket Evaluation in Early-Phase Clinical Studies and Recommendations for Labeling

Drug-Drug Interaction Clinical Decision Support Systems: Advantages, Challenges and Barriers, and Strategies to Overcome Them

Drug-drug interactions (DDIs) are a common source of preventable medical errors in inpatient and outpatient settings of both developed and developing countries. When two or more drugs are simultaneously prescribed, interactions between their effects may result in preventable adverse events such as damages to vital organs, frequent hospitalizations, prolonged length of hospital stay, and increas...

Drug-Drug Interaction Clinical Decision Support Systems: Advantages, Challenges and Barriers, and Strategies to Overcome Them

Drug-drug interactions (DDIs) are a common source of preventable medical errors in inpatient and outpatient settings of both developed and developing countries. When two or more drugs are simultaneously prescribed, interactions between their effects may result in preventable adverse events such as damages to vital organs, frequent hospitalizations, prolonged length of hospital stay, and increas...

Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies

BACKGROUND Every year, hundreds of thousands of patients experience treatment failure or adverse drug reactions (ADRs), many of which could be prevented by pharmacogenomic testing. However, the primary knowledge needed for clinical pharmacogenomics is currently dispersed over disparate data structures and captured in unstructured or semi-structured formalizations. This is a source of potential ...