Natsiavas Pantelis, Boyce Richard D, Jaulent Marie-Christine, Koutkias Vassilis
Centre for Research & Technology Hellas, Institute of Applied Biosciences, Thessaloniki, Greece.
Lab of Computing, Medical Informatics & Biomedical Imaging Technologies, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Front Pharmacol. 2018 Jun 26;9:609. doi: 10.3389/fphar.2018.00609. eCollection 2018.
Signal detection and management is a key activity in pharmacovigilance (PV). When a new PV signal is identified, the respective information is publicly communicated in the form of periodic newsletters or reports by organizations that monitor and investigate PV-related information (such as the World Health Organization and national PV centers). However, this type of communication does not allow for systematic access, discovery and explicit data interlinking and, therefore, does not facilitate automated data sharing and reuse. In this paper, we present , a novel ontology aiming to support the semantic enrichment and rigorous communication of PV signal information in a systematic way, focusing on two key aspects: (a) publishing signal information according to the FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles, and (b) exploiting automatic reasoning capabilities upon the interlinked PV signal report data. is developed as a reusable, extendable and machine-understandable model based on Semantic Web standards/recommendations. In particular, it can be used to model PV signal report data focusing on: (a) heterogeneous data interlinking, (b) semantic and syntactic interoperability, (c) provenance tracking and (d) knowledge expressiveness. is built upon widely-accepted semantic models, namely, the provenance ontology (PROV-O), the Micropublications semantic model, the Web Annotation Data Model (WADM), the Ontology of Adverse Events (OAE) and the Time ontology. To this end, we describe the design of and demonstrate its applicability as well as the reasoning capabilities enabled by its use. We also provide an evaluation of the model against the FAIR data principles. The applicability of is demonstrated by using PV signal information published in: (a) the World Health Organization's Pharmaceuticals Newsletter, (b) the Netherlands Pharmacovigilance Centre Lareb Web site and (c) the U.S. Food and Drug Administration (FDA) Drug Safety Communications, also available on the FDA Web site.
信号检测与管理是药物警戒（PV）中的一项关键活动。当识别出一个新的药物警戒信号时，相关信息会由监测和调查药物警戒相关信息的组织（如世界卫生组织和各国药物警戒中心）以定期通讯或报告的形式公开传播。然而，这种类型的交流不允许进行系统的访问、发现和明确的数据互连，因此不利于自动化的数据共享和再利用。在本文中，我们提出了一种新颖的本体，旨在以系统的方式支持药物警戒信号信息的语义丰富和严格交流，重点关注两个关键方面：（a）根据FAIR（可查找、可访问、可互操作和可再利用）数据原则发布信号信息，以及（b）利用对相互关联的药物警戒信号报告数据的自动推理能力。该本体是基于语义网标准/建议开发的可重复使用、可扩展且机器可理解的模型。具体而言，它可用于对药物警戒信号报告数据进行建模，重点关注：（a）异构数据互连，（b）语义和句法互操作性，（c）来源跟踪以及（d）知识表达能力。该本体基于广泛接受的语义模型构建，即来源本体（PROV - O）、微出版物语义模型、网络注释数据模型（WADM）、不良事件本体（OAE）和时间本体。为此，我们描述了该本体的设计，并展示了其适用性以及使用它所实现的推理能力。我们还根据FAIR数据原则对该模型进行了评估。通过使用以下来源发布的药物警戒信号信息来证明该本体的适用性：（a）世界卫生组织的《药品通讯》，（b）荷兰药物警戒中心Lareb网站，以及（c）美国食品药品监督管理局（FDA）的药品安全通讯，这些也可在FDA网站上获取。
