Detecting Potential Adverse Drug Reactions Using a Deep Neural Network Model

Abstract

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Adverse drug reactions (ADRs) are common and they are the underlying cause of over a million serious injuries and deaths each year. The most familiar method to detect ADRs is relying on spontaneous reports. Unfortunately, the low reporting rate of spontaneous reports is a serious limitation of pharmacovigilance. Objective: Our objective was to identify a method to detect potential ADRs of drugs automatically using a deep neural network (DNN). Methods: We designed a DNN model that utilizes the chemical, biological, and biomedical information of drugs to detect ADRs. This model aimed to fulfil two main purposes, identifying the potential ADRs of drugs and predicting the possible ADRs of a new drug. For improving the detection performance, we distributed representations of the target drugs in a vector space to capture the drug relationships by using the word embedding approach to process amounts of biomedical literature. Moreover, we built a mapping function to address new drugs that do not appear in the dataset. Results: We predicted the ADRs of drugs recorded up to 2012, by using the drug information and the ADRs reported up to 2009. There were contained 746 drugs and 232 new drugs which only recorded in 2012 with 1,325 ADRs. The experimental results showed that the overall performance of our model with mean average precision (MAP) at top-10 is achieved 0.523 for ADR prediction on the dataset. Conclusions: Our model was effective in identifying the potential ADRs of a drug and the possible ADRs of a new drug. Most importantly, it can detect potential ADRs irrespective of whether they have been reported in the past.