Utecht Joseph, Brochhausen Mathias, Judkins John, Schneider Jodi, Boyce Richard D
Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
Stud Health Technol Inform. 2017;245:960-964.
In this research we aim to demonstrate that an ontology-based system can categorize potential drug-drug interaction (PDDI) evidence items into complex types based on a small set of simple questions. Such a method could increase the transparency and reliability of PDDI evidence evaluation, while also reducing the variations in content and seriousness ratings present in PDDI knowledge bases. We extended the DIDEO ontology with 44 formal evidence type definitions. We then manually annotated the evidence types of 30 evidence items. We tested an RDF/OWL representation of answers to a small number of simple questions about each of these 30 evidence items and showed that automatic inference can determine the detailed evidence types based on this small number of simpler questions. These results show proof-of-concept for a decision support infrastructure that frees the evidence evaluator from mastering relatively complex written evidence type definitions.
在本研究中，我们旨在证明基于本体的系统能够根据一小组简单问题将潜在药物相互作用（PDDI）证据项分类为复杂类型。这种方法可以提高PDDI证据评估的透明度和可靠性，同时减少PDDI知识库中存在的内容和严重程度评级的差异。我们用44个正式的证据类型定义扩展了DIDEO本体。然后，我们手动注释了30个证据项的证据类型。我们测试了关于这30个证据项中每一个的少量简单问题答案的RDF/OWL表示，并表明自动推理可以基于这少量较简单的问题确定详细的证据类型。这些结果为一种决策支持基础设施提供了概念验证，该基础设施使证据评估人员无需掌握相对复杂的书面证据类型定义。
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