J Biomed Semantics. 2017 Mar 7;8(1):11. doi: 10.1186/s13326-017-0115-3.
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.
整合多源药物警戒证据有潜力推动安全信号检测与评估科学的发展。在这方面，需要开展更多研究，以探讨如何整合多个不同的证据源，同时从知识表示的角度（即语义丰富）使证据具有可计算性。现有框架表明这种整合有望取得良好成果，但所采用的证据源数量相当有限。特别是，没有一个框架是专门为支持监管和临床用例而设计的，也没有一个是通过开放式架构来添加新资源和用例的。本文讨论了一个名为“与治疗相关的大规模不良反应证据标准化系统（LAERTES）”的系统的架构和功能，该系统旨在解决这些不足。
LAERTES提供了一个标准化、开放且可扩展的架构，用于链接与药物和感兴趣的健康结局（HOIs）关联相关的证据源。使用标准术语来表示不同的实体。例如，药物和HOIs分别用RxNorm和医学系统命名法——临床术语来表示。在撰写本文时，六个证据源已加载到LAERTES证据库中，并可通过原型证据探索用户界面和一组Web应用程序编程接口服务进行访问。该系统在由观察性健康数据科学与信息学临床研究框架提供的更大软件堆栈中运行，包括由观察性医疗结局合作伙伴创建的用于观察性患者数据的关系型通用数据模型。关联数据范式的元素有助于相关证据源的系统和可扩展整合。
LAERTES原型系统提供了有用的功能，同时为进一步研究创造了机会。未来的工作将包括改进跨整合源对药物和HOI概念进行标准化的方法、在HOI概念层次结构的不同级别汇总证据，以及开发用于药物-HOI调查的更先进用户界面。
