Co-training for Extraction of Adverse Drug Reaction Mentions from Tweets

Gupta, Shashank; Gupta, Manish; Varma, Vasudeva; Pawar, Sachin; Ramrakhiyani, Nitin; Palshikar, Girish Keshav

doi:10.1007/978-3-319-76941-7_44

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…Multi-head self-attention with various features [38] has some advantages over CNN, CRNN and CNN with an attention mechanism on ADR tweet classification. Transfer learning [8], co-training [9] and multi-task learning [39] are adopted to extract ADRs, classify tweets mentioning ADRs and normalize ADRs concept, and multitask learning achieves the state-of-the-art result. With BERT performing well in many NLP tasks, researchers introduce the knowledge base and conditional random field (CRF) into BERT for the automatic classification of ADRs (text classification) and extraction of ADRs (NER) on SMM4H Shared Task 2019 [19], respectively.…”

Section: B Automatic Adr Detection From Social Textsmentioning

confidence: 99%

“…Text mining and partially supervised learning methods [3] are integrated to classify ADR (positive instances) and non-ADR messages (negative instances), and researchers employ various features such as word embedding [4], position feature [5] and medical knowledge [6] to promote the whole performance of their methods. Moreover, researchers utilize attention mechanisms [7], transfer learning [8], co-training learning [9], broad learning [10] and multitask learning [11] to learn these deep dominant features [12]. Medical resources and emotional score are merged into features that represent the semantic meaning of the text segments of different methods.…”

Section: Introductionmentioning

confidence: 99%

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An Effective Emotional Expression and Knowledge-Enhanced Method for Detecting Adverse Drug Reactions

Lin

Zheng

2020

IEEE Access

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Discovering more time-effective and a wider range of adverse drug reactions (ADRs) from social texts related to feelings concerning taking medication has recently received significant interest in pharmacovigilance research. Recognizing the posts that include ADRs is an important step for detecting ADRs from social texts. The existing systems show the unsatisfactory performance due to the insufficient expression of emotions and the inadequacy of information expression in short social texts. Although these systems exploit emotional features to improve the performance of their methods, the representation of wordlevel emotional scores is insufficient for emotional expression. Moreover, most of the systems make less use of medical knowledge to enhance the detection of the potential relationship between drugs and adverse reactions in posts. Therefore, enough expression of emotion and medical knowledge in sparse medical social texts may be explored to improve system performance. This paper proposed an effective method integrating sufficient emotional expression and medical knowledge to detect ADRs from medical tweets. First, the proposed method utilized sentence-level emotional context and word-level emotional score to learn sufficient emotional information for distinguishing between ADR and non-ADR tweets. Furthermore, a co-occurrence dictionary of each drug and its relevant ADRs was constructed by means of a medical resource (MedDRA) and drug site (www.drug.com) to help the proposed model focus on posts containing drugs and ADRs. Finally, a convolutional neural network (CNN) model on the basis of bidirectional encoder representations from transformers (BERT) performed the classification task. The proposed model achieved better overall performance than the other existing methods on two Twitter datasets (F1-scores of 72.64% and 64.98% on PSB2016 and SMM4H, respectively). INDEX TERMS Adverse drug reaction, medical knowledge, emotional context, co-occurrence dictionary, social text.

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Section: B Automatic Adr Detection From Social Textsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Effective Emotional Expression and Knowledge-Enhanced Method for Detecting Adverse Drug Reactions

Lin

Zheng

2020

IEEE Access

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“…Huynh T. et al applied convolutional recurrent neural network (CRNN), obtained by concatenating CNN with a recurrent neural network (RNN) and CNN with the additional weights (Huynh et al, 2016). Gupta S. et al utilized a semi-supervised method based on co-training (Gupta et al, 2018a). Chowdhury et al proposed a multi-task neural network framework that in addition to ADR classification learns extract ADR mentions (Chowdhury et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

“…Modern approaches for the extracting of ADRs are based on neural networks. Saldana adopted CNN for the detection of ADR relevant sentences (Miranda, 2018 (Gupta et al, 2018a). Chowdhury et al proposed a multi-task neural network framework that in addition to ADR classification learns extract ADR mentions (Chowdhury et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks

Alimova

Tutubalina

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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Detection of adverse drug reactions in postapproval periods is a crucial challenge for pharmacology. Social media and electronic clinical reports are becoming increasingly popular as a source for obtaining healthrelated information. In this work, we focus on extraction information of adverse drug reactions from various sources of biomedical textbased information, including biomedical literature and social media. We formulate the problem as a binary classification task and compare the performance of four state-of-the-art attention-based neural networks in terms of the F-measure. We show the effectiveness of these methods on four different benchmarks.

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“…Feature extraction from unstructured textual data in Natural Language Processing (NLP) developed many approaches for text mining; such as, the probabilistic approach of Bag-Of-Words (BOW) with Machine Learning (ML), the distributed representation of words approach (word embeddings), like Word2vec algorithm applied with Deep Learning (DL) [8], [9], and transfer learning, like Bidirectional Encoder Representations from Transformers (BERT) [10] and XLNet [11] techniques. The BERT model is designed to pre-train deep bidirectional representations of unlabeled text by co-conditioning both left and right context in all layers.…”

Section: Introductionmentioning

confidence: 99%

Sentence-Level Aspect-Based Sentiment Analysis for Classifying Adverse Drug Reactions (ADRs) Using Hybrid Ontology-XLNet Transfer Learning

2021

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This paper presents a hybrid ontology-XLNet sentiment analysis classification approach for sentence-level aspects. The main objective of the proposed approach allows discovering user social data considering the extracted in-depth inference about sentiment depending on the context. Thus, in this paper, we investigate the contribution of utilizing the lexicalized ontology to improve the aspect-based sentiment analysis performance through extracting the indirect relationships in user social data. The XLNet model is utilized for extracting the neighboring contextual meaning and concatenating it with each embeddings word to produce a more comprehensive context and enhance feature extraction. In the proposed approach, Bidirectional Long Short Term Memory (Bi-LSTM) networks are used for classifying the aspects in online user reviews. Various experiments considering Adverse Drug Reactions (ADRs) discovery are conducted on six drug-related social data real-world datasets to evaluate the performance of the proposed approach using several measures. Obtained experimental results show that the proposed approach outperformed other tested state-of-the-art related approaches through improving feature extraction of unstructured social media text and accordingly improving the overall accuracy of sentiment classification. A significant accuracy of 98% and F-measure of 96.4% are achieved by the proposed ADRs aspect-based sentiment analysis approach.

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Co-training for Extraction of Adverse Drug Reaction Mentions from Tweets

Cited by 11 publications

References 14 publications

An Effective Emotional Expression and Knowledge-Enhanced Method for Detecting Adverse Drug Reactions

An Effective Emotional Expression and Knowledge-Enhanced Method for Detecting Adverse Drug Reactions

Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks

Sentence-Level Aspect-Based Sentiment Analysis for Classifying Adverse Drug Reactions (ADRs) Using Hybrid Ontology-XLNet Transfer Learning

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