Large-scale adverse effects related to treatment evidence standardization (LAERTES): an open scalable system for linking pharmacovigilance evidence sources with clinical data
Boyce RD, Voss E, Huser V, Evans L, Reich C, Duke JD, Tatonetti NP, Lorberbaum T., Dumontier M, Hauben M, Wallberg M, Peng L, Dempster S, He O, Sena A, Koutkias V, Natsiavas P, Ryan P. (Knowledge Base Workgroup of the Observational Health Data Sciences and Informatics [OHDSI] collaborative). Large-scale Adverse Effects Related to Treatment Evidence Standardization (LAERTES): an Open Scalable System for Linking Pharmacovigilance Evidence Sources with Clinical Data. J Biomed Semantics. 2017 Mar 7;8(1):11. doi: 10.1186/s13326-017-0115-3. PubMed PMID: 28270198. PubMed Central PMCID: PMC5341176.
Integrating multiple sources of pharmacovigilance evidence has the potential to advance the science of safety signal detection and evaluation. In this regard, there is a need for more research on how to integrate multiple disparate evidence sources while making the evidence computable from a knowledge representation perspective (i.e., semantic enrichment). Existing frameworks suggest well-promising outcomes for such integration but employ a rather limited number of sources. In particular, none have been specifically designed to support both regulatory and clinical use cases, nor have any been designed to add new resources and use cases through an open architecture. This paper discusses the architecture and functionality of a system called Large-scale Adverse Effects Related to Treatment Evidence Standardization (LAERTES) that aims to address these shortcomings.
LAERTES provides a standardized, open, and scalable architecture for linking evidence sources relevant to the association of drugs with health outcomes of interest (HOIs). Standard terminologies are used to represent different entities. For example, drugs and HOIs are represented in RxNorm and Systematized Nomenclature of Medicine -- Clinical Terms respectively. At the time of this writing, six evidence sources have been loaded into the LAERTES evidence base and are accessible through prototype evidence exploration user interface and a set of Web application programming interface services. This system operates within a larger software stack provided by the Observational Health Data Sciences and Informatics clinical research framework, including the relational Common Data Model for observational patient data created by the Observational Medical Outcomes Partnership. Elements of the Linked Data paradigm facilitate the systematic and scalable integration of relevant evidence sources.
The prototype LAERTES system provides useful functionality while creating opportunities for further research. Future work will involve improving the method for normalizing drug and HOI concepts across the integrated sources, aggregated evidence at different levels of a hierarchy of HOI concepts, and developing more advanced user interface for drug-HOI investigations.