On Detecting Serious ADR with Data mining techniques

• Main Authors: Samantha Hwang, 黃媺雅
• Other Authors: 曹承礎
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/thnwvp

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 105 === Nowadays, more and more people occurs unexpected serious reaction after taking an FDA-approved drug. Reactions such as death, life-threatening, requires inpatient hospitalization or prolongation of existing hospitalization, persistent or significant disability/incapacity, a congenital anomaly/birth defect, or other situations will be reported to the hospitals and the drugs may be examined again. This study intends to discover the potential unexpected serious ADRs automatically by using the data mining techniques in order to improve the efficiency of detecting and to avoid the under-reporting biases. For the completeness of every patients’ records, we chose the NHIRD as our database. We want to find the strong links between the drugs, which the patients took, and the unexpected serious ADRs, which the patients suffer after taking the drug, by the association rules. Rules would be obtained and chose according to the leverage and unexlev threshold. We found that the serious ADRs of cisapride and terfenadine can be detected earlier than reporting system. The high frequency of drugs that would cause serious ADRs listed by the Danish Medicines Agency''s network were found in a high rank. Experts may examine the rules we selected and alarm the FDA for these highlighted relationships.