[Objective] To improve the performance of extracting adverse drug reactions from social media, a method is proposed to deal with non-standard texts in social media. [Methods] This method Bi-LSTM-CRF combined LSTM and CRF, and was implemented using TensorFlow. LSTM Could utilize context information, while CRF Could consider the dependence of output tags. An adverse drug reaction extraction model was constructed based on Bi-LSTM-CRF. [Results] A series of experiments were carried out on the Twitter dataset. The experimental results showed that the proposed Bi-LSTM-CRF method achieved the highest F-measure (0.7963) for adverse drug reaction extraction, compared with other methods, including CRF, forward LSTM, backward LSTM, and Bi-LSTM. [Limitations] The experiments were performed on only one corpus, and the validity of the proposed method need be verified on other data sources. [Conclusions] Combining Bi-LSTM and CRF can effectively deal with non-standard texts in social media. The constructed model in this paper can identify adverse drug reactions effectively and support relevant departments in decision-making.
朱笑笑,杨尊琦,刘婧. 基于Bi-LSTM和CRF的药品不良反应抽取模型构建*[J]. 数据分析与知识发现, 2019, 3(2): 90-97.
Xiaoxiao Zhu,Zunqi Yang,Jing Liu. Construction of an Adverse Drug Reaction Extraction Model Based on Bi-LSTM and CRF. Data Analysis and Knowledge Discovery, DOI：10.11925/infotech.2096-3467.2018.0617.
Hammond M.Users of the World, Unite! The Challenges and Opportunities of Social Media[J]. Business Horizons, 2010, 53(1): 59-68.
Andreu P J, Poon C, Merrifield R, et al.Big Data for Health[J]. IEEE Journal of Biomedical & Health Informatics, 2015, 19(4): 1.
Roughead E E, Semple S J.Medication Safety in Acute Care in Australia: Where are We Now? Part 1: A Review of the Extent and Causes of Medication Problems 2002-2008[J]. Australia & New Zealand Health Policy, 2009, 6(1): 1-12.
Liu J, Zhao S, Zhang X.An Ensemble Method for Extracting Adverse Drug Events from Social Media[J]. Artificial Intelligence in Medicine, 2016, 70(9): 62-76.
Yang C C, Yang H, Jiang L, et al.Social Media Mining for Drug Safety Signal Detection[C]// Proceedings of the 2012 International Workshop on Smart Health and Wellbeing. 2012.
Ioannis K, Azadeh N, Matthew S, et al.Analysis of the Effect of Sentiment Analysis on Extracting Adverse Drug Reactions from Tweets and Forum Posts[J]. Journal of Biomedical Informatics, 2016, 62: 148-158.
Azadeh N, Abeed S, Karen O, et al.Pharmacovigilance from Social Media: Mining Adverse Drug Reaction Mentions Using Sequence Labeling with Word Embedding Cluster Features[J]. Journal of the American Medical Informatics Association Jamia, 2015, 22(3): 671-681.
Lafferty J D, McCallum A, Pereira F C N. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data[C]//Proceedings of the 18th International Conference on Machine Learning. 2001.
Jiang K, Zheng Y.Mining Twitter Data for Potential Drug Effects[A]// Advanced Data Mining and Applications[M]. Berlin, Heidelberg: Springer, 2013: 434-443.
Leaman R, Wojtulewicz L, Sullivan R, et al.Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts to Health-Related Social Networks[C]// Proceedings of the 2010 Workshop on Biomedical Natural Language Processing. 2010: 117-125.
Liu X, Chen H.A Research Framework for Pharmacovigilance in Health Social Media: Identification and Evaluation of Patient Adverse Drug Event Reports[J]. Journal of Biomedical Informatics, 2015, 58: 268-279.
Bian J, Topaloglu U, Yu F.Towards Large-scale Twitter Mining for Drug-related Adverse Events[C]// Proceedings of the 2012 International Workshop on Smart Health and Wellbeing. 2012.
Benton A, Ungar L, Hill S, et al.Identifying Potential Adverse Effects Using the Web: A New Approach to Medical Hypothesis Generation[J]. Journal of Biomedical Informatics, 2011, 44(6): 989-989.
Wu D J H, Man C F, Kwong K, et al. Postmarketing Drug Safety Surveillance[J]. Pharmaceutical Development & Regulation, 2003, 1(4): 231-244.
Freifeld C C, Brownstein J S, Menone C M, et al.Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter[J]. Drug Safety, 2014, 37(5): 343-344.
Sampathkumar H, Chen X W, Luo B.Mining Adverse Drug Reactions from Online Healthcare Forums Using Hidden Markov Model[J]. BMC Medical Informatics & Decision Making, 2014, 14(1): 91-92.
Rastegarmojarad M, Liu H, Nambisan P.Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study[J]. JMIR Research Protocols, 2016, 5(2): e121.
Feldman R, Netzer O, Peretz A, et al.Utilizing Text Mining on Online Medical Forums to Predict Label Change due to Adverse Drug Reactions[C]// Proceedings of the 2015 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015: 1779-1788.
Metkejimenez A, Karimi S. Concept Extraction to Identify Adverse Drug Reactions in Medical Forums: A Comparison of Algorithms[OL]. arXiv Preprint, arXiv:1504.06936v1.
Lai S, Liu K, He S, et al.How to Generate a Good Word Embedding[J]. IEEE Intelligent Systems, 2016, 31(6): 5-14.
Dyer C, Ballesteros M, Ling W, et al. Transition-Based Dependency Parsing with Stack Long Short-Term Memory[OL]. arXiv Preprint, arXiv:1505.08075v1.
Chen X, Qiu X, Zhu C, et al.Long Short-Term Memory Neural Networks for Chinese Word Segmentation[C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015: 1197-1206.
Viterbi A.Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm[J]. IEEE Transactions on Informatation Theory, 1967, 13(2): 260-269.
Friggstad Z, Rezapour M, Salavatipour M R.Local Search Yields a PTAS for k-Means in Doubling Metrics[A]// Foundations of Computer Science[M]. IEEE, 2016: 365-374.
Davidian D. Feed-forward Neural Network: USA, US5438646[P].1995-08-01.
Rumelhart D E, Hinton G E, Williams R J.Learning Representations by Back-propagating Errors[J]. Nature, 1986, 323(6088): 399-421.
Graves A, Schmidhuber J.Framewise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures[J]. Neural Network, 2005, 18(5): 602-610.
Lin B Y, Xu F, Luo Z, et al.Multi-channel BiLSTM-CRF Model for Emerging Named Entity Recognition in Social Media[C]//Proceedings of the 3rd Workshop on Noisy User-generated Text. 2017: 160-165.
Palangi H, Deng L, Shen Y, et al.Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval[J]. IEEE/ACM Transactions on Audio Speech & Language Processing, 2016, 24(4): 694-707.
Soltau H, Liao H, Sak H. Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition[OL]. arXiv Preprint, arXiv:1610.09975v1.
Carlezon W A, Béguin C, Knoll A T, et al.Kappa-Opioid Ligands in the Study and Treatment of Mood Disorders[J]. Pharmacology & Therapeutics, 2009, 123(3): 334-343.
Yang E S, Kim J D, Park C Y, et al.Hyperparameter Tuning for Hidden Unit Conditional Random Fields[J]. Engineering Computations, 2017, 34(6): 2054-2062.
Xu W, Auli M, Clark S.Expected F-Measure Training for Shift-Reduce Parsing with Recurrent Neural Networks[C]// Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016: 210-220.