Year: 2013
Type: article
Abstract: OBJECTIVE: This work identified challenges associated with extraction and representation of medication-related information from publicly available electronic sources. METHODS: We gained direct observational experience through creating and evaluating the Drug Evidence Base (DEB), a repository of drug indications and adverse effects (ADEs), and supplemented this through literature review. We extracted DEB content from the National Drug File Reference Terminology, from aggregated MEDLINE co-occurrence data, and from the National Library of Medicine's DailyMed. To understand better the similarities, differences and problems with the content of DEB and the SIDER Side Effect Resource, and Vanderbilt's MEDI Indication Resource, we carried out statistical evaluations and human expert reviews. RESULTS: While DEB, SIDER, and MEDI often agreed on medication indications and side effects, cross-system shortcomings limit their current utility. The drug information resources we evaluated frequently employed multiple, disparate vaguely related UMLS concepts to represent a single specific clinical drug indication or adverse effect. Thus, evaluations comparing drug-indication and drug-ADE coverage for such resources will encounter substantial numbers of false negative and false positive matches. Furthermore, our review found that many indication and ADE relationships are too complex - logically and temporally - to represent within existing systems. CONCLUSION: To enhance applicability and utility, future drug information systems deriving indications and ADEs from public resources must represent clinical concepts uniformly and as precisely as possible. Future systems must also better represent the inherent complexity of indications and ADEs.
Source: Applied Clinical Informatics
Authors: J.C. Denny, Q. Chen, H. Nian, A. Spickard, S. T. Rosenbloom, R. A. Miller, J.C. Smith
Institutions: Vanderbilt University, Vanderbilt University Medical Center
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Awards: Pharmacogenomics of Arrhythmia Therapy, A New Approach to Correct Verification Bias Using Auxiliary Information, National Infrastructure for Standardized and Portable EHR Phenotyping Algorithms, Evidence-based Diagnostic Tools for Translational and Clinical Research (eTfor2), G3345424140, MOMENT (Monitoring for Outpatient Medication Effects and New Toxicities) in TIME, Beyond PheWAS: Recognition of Phenotype Patterns for Discovery and Translation, Vanderbilt Biomedical Informatics Training Program
OpenAlex ID: W2117953109