Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features

By Azadeh Nikfarjam, Abeed Sarker, Karen O'Connor, Rachel E. Ginn, Graciela Gonzalez-Hernandez. J. Am. Medical Informatics Assoc., 2015

565 citations recorded by the source corpus.

Abstract Objective Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media. Methods We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words’ semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique. Results ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance. Conclusion It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets.

Subfields: Artificial Intelligence, Molecular Biology, Toxicology

View Paper: https://www.semanticscholar.org/paper/ee8085bd00f437318cce5b919b47233de8c5f6ff

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Citations in the corpus

A Social Media Study on the Effects of Psychiatric Medication Use (2019, background): “Social media has also facilitated the identification of adverse drug reactions at the population level using self-reports (Lardon et al. 2015) as well as the mentions of side effects of adverse drug reaction on Twitter (Nikfarjam et al. 2015).”
Attention is not not Explanation (2019, background): “Citing Ross et al. (2017), Jain and Wallace’s requisite for attention distributions to be used as explanation is that there must only exist one or a few closely-related correct explanations for a model prediction.”
Improving chemical disease relation extraction with rich features and weakly labeled data (2016, methodology): “It may be challenging to directly apply our method to full-length articles (because considerable time may be required to process linguistic analyses) or abbreviated social media text [3, 40].”
Utilizing Social Media Data for Pharmacovigilance: A Review (2015, methodology): “Most research tasks have been designed such that evaluations could be performed using existing metrics such as Recall , Precision , F-score and Accuracy [17,34,42,43,45,46,50,57,58,38,39,41,62,65].”
Deep learning for healthcare: review, opportunities and challenges (2018, unknown): “social media for pharmacovigilance [104, 105]), wearable devices,”
Attention is not Explanation (2019, unknown): “Twitter Adverse Drug Reaction dataset (Nikfarjam et al., 2015).”
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning (2020, background): “Furthermore, pre-trained embeddings can directly solve subtasks in information retrieval systems, such as expanding search queries to include related terms [21, 39, 61], predicting question-answer relatedness [14, 35], deriving the word’s kmeans cluster [54], and more.”