A Generalized Model for Predicting Potential Adverse Drug Reactions by Deep Neural Network

論文翻譯標題: 基於深度學習建置廣泛性的潛在藥物不良反應預測模型
  • 林 佩儒

學生論文: Master's Thesis


As defined by the World Health Organization adverse drug reaction (ADR) is “a response to a medicine which is noxious and unintended and which occurs at doses normally used in man ” Adverse drug reaction is a crucial topic in drug safety that should not be neglected Because of the limitation of clinical trials the detection of ADRs relies on spontaneous reports However low reporting rates and the under-reporting of spontaneous report become a problem on pharmacovigilance Therefore if we can detect the adverse drug reaction earlier the drug safety evaluators can assess the potential adverse drug reaction and enhance the pharmacovigilance Previous studies usually treat the prediction of adverse drug reaction as a classification problem without consideration of time It ignores the chronological orders of the data of adverse drug reactions During the drug development process adverse drug reaction will be detected at different stages corresponding to a lot of published literature These data are comprehensive properties of drugs that are used to predict the adverse drug reactions Without the proper time order the prediction of ADR will not be as accurate This study aims to develop a generalized adverse drug prediction model that can utilize the properties of the drug to predict potential ADR even without previous adverse drug reaction record We used deep learning combining with chemical properties biological properties and the drug embedding we extracted from literature mining to assess the potential adverse drug reaction The drug embedding we learned from large-scale literature can effectively enhance the performance The drug2vec expansion model can represent the drug we don’t have any property before Most important of all we can predict the potential adverse drug reaction whether or not the adverse drug reaction was recorded before
獎項日期2017 8月 15
監督員Jung-Hsien Chiang (Supervisor)