Detecting Potential Adverse Drug Reactions Using a Deep Neural Network Model

Background Adverse drug reactions (ADRs) are common and 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 The objective of this study 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 fulfill 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 using the word-embedding approach to process substantial biomedical literature. Moreover, we built a mapping function to address new drugs that do not appear in the dataset. Results Using the drug information and the ADRs reported up to 2009, we predicted the ADRs of drugs recorded up to 2012. There were 746 drugs and 232 new drugs, which were only recorded in 2012 with 1325 ADRs. The experimental results showed that the overall performance of our model with mean average precision at top-10 achieved is 0.523 and the rea under the receiver operating characteristic curve (AUC) score achieved is 0.844 for ADR prediction on the dataset. Conclusions Our model is 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.

[1]  S. R. Fine,et al.  ADVERSE DRUG REACTIONS , 2009, BMJ : British Medical Journal.

[2]  D. Greenblatt,et al.  A method for estimating the probability of adverse drug reactions , 1981, Clinical pharmacology and therapeutics.

[3]  W. Inman,et al.  Under-reporting of adverse drug reactions. , 1985, British medical journal.

[4]  P. Corey,et al.  Incidence of Adverse Drug Reactions in Hospitalized Patients , 2012 .

[5]  A. Bate,et al.  A Bayesian neural network method for adverse drug reaction signal generation , 1998, European Journal of Clinical Pharmacology.

[6]  I. Edwards,et al.  Adverse drug reactions: definitions, diagnosis, and management , 2000, The Lancet.

[7]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[8]  Padmini Srinivasan,et al.  Text mining: Generating hypotheses from MEDLINE , 2004, J. Assoc. Inf. Sci. Technol..

[9]  L. Hazell,et al.  Under-Reporting of Adverse Drug Reactions , 2006, Drug safety.

[10]  M. Kulldorff,et al.  Early detection of adverse drug events within population‐based health networks: application of sequential testing methods , 2007, Pharmacoepidemiology and drug safety.

[11]  Bin Chen,et al.  PubChem as a Source of Polypharmacology , 2009, J. Chem. Inf. Model..

[12]  P. Bork,et al.  A side effect resource to capture phenotypic effects of drugs , 2010, Molecular systems biology.

[13]  R. Tibshirani,et al.  Regression shrinkage and selection via the lasso: a retrospective , 2011 .

[14]  Ben Y. Reis,et al.  Predicting Adverse Drug Events Using Pharmacological Network Models , 2011, Science Translational Medicine.

[15]  C. Friedman,et al.  A drug-adverse event extraction algorithm to support pharmacovigilance knowledge mining from PubMed citations. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[16]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[17]  Russ B. Altman,et al.  A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports , 2012, J. Am. Medical Informatics Assoc..

[18]  Hua Xu,et al.  Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs , 2012, J. Am. Medical Informatics Assoc..

[19]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[20]  J. Chen,et al.  Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures , 2013, Proteomics.

[21]  Bin Yao,et al.  Safety Monitoring in Clinical Trials , 2013, Pharmaceutics.

[22]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[23]  Ying Li,et al.  A method for controlling complex confounding effects in the detection of adverse drug reactions using electronic health records , 2014, J. Am. Medical Informatics Assoc..

[24]  Dymitr Ruta.  Robust method of sparse feature selection for multi-label classification with Naive Bayes , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[25]  E. Sugawara,et al.  Properties of AdeABC and AdeIJK Efflux Systems of Acinetobacter baumannii Compared with Those of the AcrAB-TolC System of Escherichia coli , 2014, Antimicrobial Agents and Chemotherapy.

[26]  Quoc V. Le,et al.  Distributed Representations of Sentences and Documents , 2014, ICML.

[27]  Zhongming Zhao,et al.  Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. , 2014, Journal of the American Medical Informatics Association : JAMIA.

[28]  Ming Yang,et al.  Filtering big data from social media - Building an early warning system for adverse drug reactions , 2015, J. Biomed. Informatics.

[29]  Guan Wang,et al.  A method for systematic discovery of adverse drug events from clinical notes , 2015, J. Am. Medical Informatics Assoc..

[30]  Cécile Paris,et al.  Text and Data Mining Techniques in Adverse Drug Reaction Detection , 2015, ACM Comput. Surv..

[31]  Sanja Fidler,et al.  Skip-Thought Vectors , 2015, NIPS.

[32]  Michael J. Keiser,et al.  Systems pharmacology augments drug safety surveillance , 2014, Clinical pharmacology and therapeutics.

[33]  Abeed Sarker,et al.  Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features , 2015, J. Am. Medical Informatics Assoc..

[34]  Zhiyong Lu,et al.  Text mining for precision medicine: automating disease-mutation relationship extraction from biomedical literature , 2016, J. Am. Medical Informatics Assoc..

[35]  Joseph Finkelstein,et al.  Automated Summarization of Publications Associated with Adverse Drug Reactions from PubMed , 2016, CRI.

[36]  J. Coleman,et al.  Adverse drug reactions. , 2016, Clinical medicine.

[37]  Peer Bork,et al.  The SIDER database of drugs and side effects , 2015, Nucleic Acids Res..

[38]  Jian Zhang,et al.  Social Friend Recommendation Based on Multiple Network Correlation , 2016, IEEE Transactions on Multimedia.

[39]  Tomas Mikolov,et al.  Enriching Word Vectors with Subword Information , 2016, TACL.

[40]  David S. Wishart,et al.  DrugBank 5.0: a major update to the DrugBank database for 2018 , 2017, Nucleic Acids Res..