Development of searching similar cases from clinical database with relational infomation of medical terms

目的：开发1）自动提取基于计算机的患者入院报告中包含的重要临床术语。2)在保持语义关系网络的前提下，对病例的临床意义进行表征。方法：为开发基于计算机的入院报告系统做准备，我们分析了所提取的医学术语的语义结构，并构建了从临床、报告和病历中提取的医学术语的叙词表数据库。此外，我们开发了一个标准化的疾病的临床术语，并构建了结构化的层次结构的术语。最后，我们开发了一个入院报告的数据库的评估研究和提取的医学术语的关系。结果：我们构建了一个大型的关系语义结构，包含约20000疾病名称和60000索引词。我们使用这个数据库分析了800份入院报告，并提取了重要的医学术语和共现图。使用验证方法，我们评估的结果和17例被评为良好，3例被评为差cases.Discussion：本研究的主要贡献是，我们构建了医学术语的语义数据库，为未来的研究自然语言处理的医学文本库。我们必须增加一些症状和体征的术语。对于相似病例的自动提取，是否存在体征或症状是最重要的信息，构建必要的数据库是未来要实现的问题

Purpose : development of 1)automatic extraction of significant clinical terms contained in computer-based admission report of patients. 2)representation of clinical meaning of cases with keeping semantic network of relationships. among medical terms extracted, 3)extraction of similar cases from the database, 4)evaluation of the efficiency of the above methods using cross-validation methods.Methods : For the preparation of developing a compute-based admission reporting system, we analyzed the semantic structure of the extracted medical terms, and constructed a thesaurus database of medical terms extracted from clinical, report and medical records. Further, we developed a standardized clinical terminology of diseases and constructed the structured hierarchy of the terminology. Finally we developed database of an admission reports for the evaluation study and extracted the relations of medical terms.Results : We constructed a large relational semantic structure that contains about 20000 disease names and 60000 indexing terms. We analyzed 800 admission reports using this database and extracted significant medical terms and co-occurrence graph. Using validation method, we evaluated the results and 17 cases were good and 3 cases were evaluated as poor cases.Discussion: The main contribution of this study was that we constructed the semantic database of medical terminology for the future study for natural language processing of medical text-base. We have to add terminology of signs and symptoms. Concerning automatic extraction of similar cases, whether a sign or a symptom exists or not is the most significant information and constructing the necessary database for the purpose is the future issue to be achieved