ArticleJournal of biomedical semantics2017
Large-scale adverse effects related to treatment evidence standardization (LAERTES): an open scalable system for linking pharmacovigilance evidence sources with clinical data.
Article in Journal of biomedical semantics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches.Frontiers in pharmacology · 2019Pooled it
- COVID-19 vaccination effectiveness rates by week and sources of bias: a retrospective cohort study.BMJ open · 2022Article
- COVID-19 vaccination effectiveness rates by week and sources of bias.medRxiv : the preprint server for health sciences · 2021Article
- An updated, computable MEDication-Indication resource for biomedical research.Scientific reports · 2021Article
- Utilizing Advanced Technologies to Augment Pharmacovigilance Systems: Challenges and Opportunities.Therapeutic innovation & regulatory science · 2020Review
- Complementing Observational Signals with Literature-Derived Distributed Representations for Post-Marketing Drug Surveillance.Drug safety · 2020Article
- Exploring Novel Computable Knowledge in Structured Drug Product Labels.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- A 2018 workshop: vaccine and drug ontology studies (VDOS 2018).BMC bioinformatics · 2019Article
- From Data Silos to Standardized, Linked, and FAIR Data for Pharmacovigilance: Current Advances and Challenges with Observational Healthcare Data.Drug safety · 2019Article
- E-Synthesis: A Bayesian Framework for Causal Assessment in Pharmacosurveillance.Frontiers in pharmacology · 2019Article
- ODAL: A one-shot distributed algorithm to perform logistic regressions on electronic health records data from multiple clinical sites.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- Clinical Research Informatics: Contributions from 2017.Yearbook of medical informatics · 2018Review
- Recent Developments in Clinical Terminologies - SNOMED CT, LOINC, and RxNorm.Yearbook of medical informatics · 2018Article
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- Accuracy of an automated knowledge base for identifying drug adverse reactions.Journal of biomedical informatics · 2017Article
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Authors and funding
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Abstract
backgroundIntegrating 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.
resultsLAERTES 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.
conclusionsThe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.