Si Yuqi, Weng Chunhua
Department of Biomedical Informatics, Columbia University, New York, NY, USA.
Stud Health Technol Inform. 2017;245:950-954.
Eligibility criteria are important for clinical research protocols or clinical practice guidelines for determining who qualify for studies and to whom clinical evidence is applicable, but the free-text format is not amenable for computational processing. In this paper, we described a practical method for transforming free-text clinical research eligibility criteria of Alzheimer's clinical trials into a structured relational database compliant with standards for medical terminologies and clinical data models. We utilized a hybrid natural language processing system and a concept normalization tool to extract medical terms in clinical research eligibility criteria and represent them using the OMOP Common Data Model (CDM) v5. We created a database schema design to store syntactic relations to facilitate efficient cohort queries. We further discussed the potential of applying this method to trials on other diseases and the promise of using it to accelerate clinical research with electronic health records.
纳入标准对于临床研究方案或临床实践指南很重要，用于确定谁有资格参与研究以及临床证据适用于谁，但自由文本格式不便于进行计算处理。在本文中，我们描述了一种实用方法，可将阿尔茨海默病临床试验的自由文本临床研究纳入标准转换为符合医学术语和临床数据模型标准的结构化关系数据库。我们利用混合自然语言处理系统和概念规范化工具来提取临床研究纳入标准中的医学术语，并使用OMOP通用数据模型（CDM）v5来表示它们。我们创建了一个数据库模式设计来存储句法关系，以促进高效的队列查询。我们进一步讨论了将此方法应用于其他疾病试验的潜力，以及使用它来加速电子健康记录临床研究的前景。
