||A Generalized Model for Predicting Potential Adverse Drug Reactions by Deep Neural Network
||Institute of Computer Science and Information Engineering
adverse drug reaction
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.
List of Tables VIII
List of Figures IX
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Research Objective 2
1.4 Thesis Organization 3
Chapter 2 Related Work 4
2.1 Pharmacovigilance 4
2.2 Detection of Adverse Drug Reaction 5
2.2.1 Detect ADR from Omics Data 5
2.2.2 Detect ADR from Biomedical Literature 6
Chapter 3 Materials and Methods 7
3.1 Study of Our Research Work 8
3.2 Data Collection 10
3.2.1 Collect the Drugs and the ADR 10
3.2.2 Extract Chemical and Biological Properties 11
3.3 Data Processing 13
3.3.1 ADR Vector Notation 13
3.3.2 Drug Embedding 14
3.3.3 Drug2Vec Expansion 15
3.4 Generalized Adverse Drug Reaction Prediction Model 16
3.4.1 Model Architecture 17
3.4.2 Model Building 18
Chapter 4 Experiments 20
4.1 Experimental Design 20
4.2 Survey of Drug Embedding 21
4.3 Performance Evaluation 22
4.3.1 Evaluation of Model 23
4.3.2 Evaluation of Features 24
4.3.3 Discussion on Model Depth 25
4.4 Evaluation of Generalized Model 26
4.4.1 Evaluation of Detection and Prediction 26
4.4.2 Evaluation of Drug2Vec Expansion 27
4.4.3 Case Study 27
Chapter 5 Conclusions and Future Work 29
5.1 Conclusions 29
5.2 Future Work 30
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