Research on Converting the Narrative Text of Scientific Research Articles into Structured Documents by Natural Language Processing

• Main Authors: Ching-Yun Lin, 林青芸
• Other Authors: Der-Ming Liou
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/kju89t

碩士 === 國立陽明大學 === 生物醫學資訊研究所 === 105 === Background: In NHIRD studies, researchers must define a set of suitable eligibility criteria of study samples. Several researches focus on eligibility criteria have suggested that study results of the same disease would vary with different setting of eligibility criteria. Therefore, the establishment of eligibility criteria is an important step in the process of study design. Aim: To convert the narrative eligibility criteria into structured form, we built a series of R-based text processing methods to analyze the eligibility criteria in NHIRD articles. Methods: There were 2517 NHIRD papers used to build up the text processing tool. We classified their study type from article title, identifying medical concepts and abbreviations, detecting basic demographic characteristics and limitation of specialist, extracting diagnosis codes and temporal relationship, then created the structured eligibility criteria XML files. Results: Although there is still room for improvement on visit of medical utilization and temporal relationship identifying, the high performance in detecting abbreviations, age restrictions and limitation of specialists still show the system useful for eligibility criteria analysis.