A Question-Retrieval-Based Patient History Inquery System for Computerized Virtual Patient

• Main Authors: Chang-Gang Yang, 楊長鋼
• Other Authors: Wen-Cheng Lin
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/96024179118908295676

碩士 === 慈濟大學 === 醫學資訊學系碩士班 === 102 === There are more and more courses in medicine using problem-based learning (PBL) method to teach students. Students can strengthen their medical knowledge by practicing diagnosis through a designed problem. A PBL course needs many tutors to guide and to monitor the learning process of groups of students. In order to reduce the workload of tutors, Computerized Virtual Patient (CVP) system has been proposed to support the learning process. In CVP system, students gather medical history information of patients by asking the patient history inquery subsystem. In previous study, the performance of patient history inquery subsystem was not very good. In this study, we try to enhance the patient history inquery subsystem. We adopt the architecture of question retrieval to find the answer of the student’s questions. Three steps are taken while answering a student’s question: question preprocessing, question classification, and question similarity calculation. Question preprocessing contains five steps: Chinese word segmentation, long word identification, synonym normalization, removing stop words, and replacement of semantic class. Question classification module uses question patterns to find the class a question belongs to. After classification, the similarities between the student’s question and the predefined questions in PBL problem with the same class as the student’s question are calculated. In addition to the cosine-similarity of vector-space-model, we also consider the overlapping words with the same semantic class in two questions while calculating question similarity. Experimental results showed that the accuracy of finding correct question comes to 84.65% when the similarity threshold was set to 0.7. The high accuracy can reduce the chance of providing candidate questions to students. And students can develop their ability of logical reasoning and diagnosis skill more effectively.