SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media

Artificial Intelligence in Medicine - Tập 84 - Trang 34-49 - 2018
Jing Liu1, Songzheng Zhao2, Gang Wang3
1School of Management Science and Engineering, Tianjin University of Finance and Economics, Tianjin 300222, PR China
2School of Management, Northwestern Polytechnical University, Xi′an, Shaanxi 710072, PR China
3School of Management, Hefei University of Technology, Hefei, Anhui 230009, PR China

Tài liệu tham khảo

Chee, 2011, Predicting adverse drug events from personal health messages, 217 Ji, 2011, A potential causal association mining algorithm for screening adverse drug reactions in postmarketing surveillance, IEEE Trans Inf Technol Biomed, 15, 428, 10.1109/TITB.2011.2131669 Leaman, 2010, Towards internet-age pharmacovigilance: extracting adverse drug reactions from user posts to health-related social networks, 117 Hazell, 2006, Under-reporting of adverse drug reactions, Drug Saf, 29, 385, 10.2165/00002018-200629050-00003 Yang, 2012, Social media mining for drug safety signal detection, 33 Andreu-Perez, 2015, Big data for health, IEEE J Biomed Health Inform, 19, 1193, 10.1109/JBHI.2015.2450362 Zeng, 2010, Social media analytics and intelligence, IEEE Intell Syst, 25, 13, 10.1109/MIS.2010.151 Denecke, 2009, How valuable is medical social media data? Content analysis of the medical web, Inf. Sci., 179, 1870, 10.1016/j.ins.2009.01.025 Sarker, 2015, Utilizing social media data for pharmacovigilance: a review, J Biomed Inform, 54, 202, 10.1016/j.jbi.2015.02.004 Yang, 2015, Filtering big data from social media–building an early warning system for adverse drug reactions, J Biomed Inform, 54, 230, 10.1016/j.jbi.2015.01.011 Sarker, 2015, Portable automatic text classification for adverse drug reaction detection via multi-corpus training, J Biomed Inform, 53, 196, 10.1016/j.jbi.2014.11.002 Liu, 2015, A research framework for pharmacovigilance in health social media: identification and evaluation of patient adverse drug event reports, J Biomed Inform, 58, 268, 10.1016/j.jbi.2015.10.011 Nikfarjam, 2015, Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features, J Am Med Inf Assoc, 22, 671, 10.1093/jamia/ocu041 Korkontzelos, 2016, Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts, J Biomed Inform, 62, 148, 10.1016/j.jbi.2016.06.007 Liu, 2016, An ensemble method for extracting adverse drug events from social media, Artif Intell Med, 62, 10.1016/j.artmed.2016.05.004 Liu, 2014, Identifying adverse drug events from health social media: a case study on heart disease discussion forums, 25 Zhou, 2011, When semi-supervised learning meets ensemble learning, Front Electr Electron Eng China, 6, 6, 10.1007/s11460-011-0126-2 Breiman, 1996, Bagging predictors, Mach Learn, 24, 123, 10.1007/BF00058655 Schapire, 1990, The strength of weak learnability, Mach Learn, 5, 197, 10.1007/BF00116037 Ho, 1998, The random subspace method for constructing decision forests, IEEE Trans Pattern Anal Mach Intell, 20, 832, 10.1109/34.709601 Rosenberg, 2005, Semi-supervised self-training of object detection models, 29 Blum, 1998, Combining labeled and unlabeled data with co-training, 92 Bian, 2012, Towards large-scale twitter mining for drug-related adverse events, 25 Jiang, 2013 Freifeld, 2014, Digital drug safety surveillance: monitoring pharmaceutical products in Twitter, Drug Saf, 37, 343, 10.1007/s40264-014-0155-x Nikfarjam, 2011, Pattern mining for extraction of mentions of adverse drug reactions from user comments, 1019 Yang, 2014, Postmarketing drug safety surveillance using publicly available health-Consumer-Contributed content in social media, ACM Trans Manage Inf Syst, 5, 2, 10.1145/2576233 Liu, 2013, AZDrugMiner: an information extraction system for mining patient-reported adverse drug events in online patient forums, 134 Patki, 2014 Huynh, 2016 Lee, 2017, Adverse drug event detection in tweets with semi-supervised convolutional neural networks, Proceedings of the 26th international conference on world wide web, 705, 10.1145/3038912.3052671 Benton, 2011, Identifying potential adverse effects using the web: a new approach to medical hypothesis generation, J Biomed Inform, 44, 989, 10.1016/j.jbi.2011.07.005 Segura-Bedmar, 2015, Exploring Spanish health social media for detecting drug effects, BMC Med Inform Decis Mak, 15, 1, 10.1186/1472-6947-15-S2-S6 Sampathkumar, 2014, Mining adverse drug reactions from online healthcare forums using hidden Markov model, BMC Med Inform Decis Mak, 14, 1, 10.1186/1472-6947-14-91 Yates, 2015, Extracting adverse drug reactions from social media, AAAI, 2460 Wang, 2014, Sideeffectptm: an unsupervised topic model to mine adverse drug reactions from health forums, Proceedings of the 5th ACM conference on bioinformatics, computational biology and health informatics, 321, 10.1145/2649387.2649398 Nigam, 2000, Analyzing the effectiveness and applicability of co-training, Proceedings of the ninth international conference on information and knowledge management, 86 Zhou, 2010, Semi-supervised learning by disagreement, Know Inf Syst, 24, 415, 10.1007/s10115-009-0209-z Goldman, 2000, Enhancing supervised learning with unlabeled data, ICML, 327 Zhou, 2004, Democratic co-learning, 16th IEEE international conference on tools with artificial intelligence, 2004. ICTAI 2004, 594 Zhou, 2005, Tri-training: exploiting unlabeled data using three classifiers, IEEE Trans Knowl Data Eng, 17, 1529, 10.1109/TKDE.2005.186 Li, 2007, Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples, IEEE Trans Syst Man Cybern Part A: Syst Hum, 37, 1088, 10.1109/TSMCA.2007.904745 Hady, 2008, Co-training by committee: a generalized framework for semi-supervised learning with committees, Int J Softw Inform, 2, 95 Jiang, 2013, Inter-training: exploiting unlabeled data in multi-classifier systems, Knowl-Based Syst, 45, 8, 10.1016/j.knosys.2013.01.028 Wang, 2008, A random subspace method for co-training, IEEE international joint conference on neural networks, 2008. IJCNN 2008 (IEEE world congress on computational intelligence), 195, 10.1109/IJCNN.2008.4633789 Yaslan, 2010, Co-training with relevant random subspaces, Neurocomputing, 73, 1652, 10.1016/j.neucom.2010.01.018 Sun, 2011, Multiple-view multiple-learner semi-supervised learning, Neural Process Lett, 34, 229, 10.1007/s11063-011-9195-8 Zhang, 2013, Exploiting unlabeled data to enhance ensemble diversity, Data Mining Knowl Discov, 26, 98, 10.1007/s10618-011-0243-9 Li, 2008, Kernel-based learning for biomedical relation extraction, J Am Soc Inf Sci Technol, 59, 756, 10.1002/asi.20791 Sun, 2015, Extracting drug–drug interactions from literature using a rich feature-based linear kernel approach, J Biomed Inform, 39, 23 Xiao, 2005, Protein–protein interaction extraction: a supervised learning approach, Proc Symp Semant Mining Biomed, 51 Kim, 2008, Kernel approaches for genic interaction extraction, Bioinformatics, 24, 118, 10.1093/bioinformatics/btm544 Segura-Bedmar, 2011, Using a shallow linguistic kernel for drug–drug interaction extraction, J Biomed Inform, 44, 789, 10.1016/j.jbi.2011.04.005 Giuliano, 2006, Exploiting shallow linguistic information for relation extraction from biomedical literature, 401 Kambhatla, 2004, Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations Zhou, 2007, Extracting relation information from text documents by exploring various types of knowledge, Inf Process Manage, 43, 969, 10.1016/j.ipm.2006.09.012 Minard, 2011, Feature selection for Drug–Drug Interaction detection using machine-learning based approaches, 43 Bunescu, 2005, A shortest path dependency kernel for relation extraction, 724 Zhu, 2005 Ditterrich, 1997, Machine learning research: four current direction, AI Mag, 4, 97 Wang, 2014, Sentiment classification: the contribution of ensemble learning, Decis Support Syst, 57, 77, 10.1016/j.dss.2013.08.002 Polikar, 2006, Ensemble based systems in decision making, IEEE Circ Syst Mag, 6, 21, 10.1109/MCAS.2006.1688199 Rokach, 2010, Ensemble-based classifiers, Artif Intell Rev, 33, 1, 10.1007/s10462-009-9124-7 Bradley, 1997, The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recogn, 30, 1145, 10.1016/S0031-3203(96)00142-2 Vapnik, 2000 Zhang, 2014, Semi-supervised learning combining co-training with active learning, Expert Syst Appl, 41, 2372, 10.1016/j.eswa.2013.09.035