Li Fang, Rao Guozheng, Du Jingcheng, Xiang Yang, Zhang Yaoyun, Selek Salih, Hamilton Jane Elizabeth, Xu Hua, Tao Cui
The University of Texas Health Science Center at Houston, USA.
Tianjin University, China.
Health Informatics J. 2020 Jun;26(2):726-737. doi: 10.1177/1460458219832059. Epub 2019 Mar 7.
The Research Domain Criteria, launched by the National Institute of Mental Health, is a new dimensional and interdisciplinary research framework for mental disorders. The Research Domain Criteria matrix is its core part. Since an ontology has the strengths of supporting semantic inferencing and automatic data processing, we would like to transform the Research Domain Criteria matrix into an ontological structure. In terms of data normalization, which is the essential part of an ontology representation, the Research Domain Criteria elements (mainly in the Units of Analysis) have some limitations. In this article, we propose a series of solutions to improve data normalization of the Research Domain Criteria elements in the Units of Analysis, including leveraging standard terminologies (i.e. the Unified Medical Language System Metathesaurus), context-combining queries, and domain expertise. The evaluation results show the positive (Yes) percentage is more than 80 percent, indicating our work is favorably received by the mental health professionals, and we have formed a good data foundation for the Research Domain Criteria ontological representation in the future work.
由美国国立精神卫生研究所发起的研究领域标准（Research Domain Criteria）是一种针对精神障碍的全新维度和跨学科研究框架。研究领域标准矩阵是其核心部分。由于本体具有支持语义推理和自动数据处理的优势，我们希望将研究领域标准矩阵转化为本体结构。在作为本体表示重要组成部分的数据规范化方面，研究领域标准元素（主要在分析单元中）存在一些局限性。在本文中，我们提出了一系列解决方案，以改进分析单元中研究领域标准元素的数据规范化，包括利用标准术语（即统一医学语言系统叙词表）、上下文组合查询和领域专业知识。评估结果表明，肯定（是）百分比超过80%，这表明我们的工作受到了心理健康专业人员的好评，并且我们在未来的工作中为研究领域标准本体表示奠定了良好的数据基础。
