Department of Biofunctional Medicine and Diagnostics, Kyung Hee University, Hoegi-dong, Seoul, Republic of Korea.
PLoS One. 2013;8(3):e59241. doi: 10.1371/journal.pone.0059241. Epub 2013 Mar 15.
Extracting useful and meaningful patterns from large volumes of text data is of growing importance. In the present study we analyze vast amounts of prescription data, generated from the book of oriental medicine to identify the relationships between the symptoms and the associated medicines used to treat these symptoms. The oriental medicine book used in this study (called Bangyakhappyeon) contains a large number of prescriptions to treat about 54 categorized symptoms and lists the corresponding herbal materials. We used an association rule algorithm combined with network analysis and found useful and informative relationships between the symptoms and medicines.
从大量文本数据中提取有用且有意义的模式变得越来越重要。在本研究中,我们分析了大量的处方数据,这些数据是从东方医学书籍中生成的,以确定症状与用于治疗这些症状的相关药物之间的关系。本研究中使用的东方医学书籍(称为 Bangyakhappyeon)包含了大量治疗约 54 种分类症状的处方,并列出了相应的草药材料。我们使用关联规则算法结合网络分析,发现了症状和药物之间有用且有信息的关系。