Yamagata Yuki, Kozaki Kouji, Imai Takeshi, Ohe Kazuhiko, Mizoguchi Riichiro
ISIR, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, Japan.
Department of Medical Informatics, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan.
J Biomed Semantics. 2014 May 21;5:23. doi: 10.1186/2041-1480-5-23. eCollection 2014.
Recently, exchanging data and information has become a significant challenge in medicine. Such data include abnormal states. Establishing a unified representation framework of abnormal states can be a difficult task because of the diverse and heterogeneous nature of these states. Furthermore, in the definition of diseases found in several textbooks or dictionaries, abnormal states are not directly associated with the corresponding quantitative values of clinical test data, making the processing of such data by computers difficult.
We focused on abnormal states in the definition of diseases and proposed a unified form to describe an abnormal state as a "property," which can be decomposed into an "attribute" and a "value" in a qualitative representation. We have developed a three-layer ontological model of abnormal states from the generic to disease-specific level. By developing an is-a hierarchy and combining causal chains of diseases, 21,000 abnormal states from 6000 diseases have been captured as generic causal relations and commonalities have been found among diseases across 13 medical departments.
Our results showed that our representation framework promotes interoperability and flexibility of the quantitative raw data, qualitative information, and generic/conceptual knowledge of abnormal states. In addition, the results showed that our ontological model have found commonalities in abnormal states among diseases across 13 medical departments.
近年来，数据和信息交换已成为医学领域的一项重大挑战。此类数据包括异常状态。由于这些状态的多样性和异质性，建立异常状态的统一表示框架可能是一项艰巨的任务。此外，在几本教科书或词典中发现的疾病定义中，异常状态与临床检测数据的相应定量值没有直接关联，这使得计算机处理此类数据变得困难。
我们聚焦于疾病定义中的异常状态，并提出了一种统一形式，将异常状态描述为一种“属性”，在定性表示中可分解为“属性”和“值”。我们已经从通用到疾病特定层面开发了一个三层异常状态本体模型。通过建立一个“是一个”层次结构并结合疾病的因果链，从6000种疾病中捕获了21000种异常状态作为通用因果关系，并在13个医学科室的疾病之间发现了共性。
我们的结果表明，我们的表示框架促进了异常状态的定量原始数据、定性信息和通用/概念知识的互操作性和灵活性。此外，结果表明我们的本体模型在13个医学科室的疾病异常状态中发现了共性。
