Paper Info
Title
Identifying Adverse Drug Reaction-Related Text from Social Media: A Multi-View Active Learning Approach with Various Document Representations
Abstract
Adverse drug reactions (ADRs) are a huge public health issue. Identifying text that mentions ADRs from a large volume of social media data is important. However, we need to address two challenges for high-performing ADR-related text detection: the data imbalance problem and the requirement of simultaneously using data-driven information and handcrafted information. Therefore, we propose an approach named multi-view active learning using domain-specific and data-driven document representations (MVAL4D), endeavoring to enhance the predictive capability and alleviate the requirement of labeled data. Specifically, a new view-generation mechanism is proposed to generate multiple views by simultaneously exploiting various document representations obtained using handcrafted feature engineering and by performing deep learning methods. Moreover, different from previous active learning studies in which all instances are chosen using the same selection criterion, MVAL4D adopts different criteria (i.e., confidence and informativeness) to select potentially positive instances and potentially negative instances for manual annotation. The experimental results verify the effectiveness of MVAL4D. The proposed approach can be generalized to many other text classification tasks. Moreover, it can offer a solid foundation for the ADR mention extraction task, and improve the feasibility of monitoring drug safety using social media data.
Year
DOI
Venue
2022
10.3390/info13040189
INFORMATION
Keywords
DocType
Volume
adverse drug reaction, multi-view active learning, selection strategy, document representation
Journal
13
Issue
ISSN
Citations 
4
2078-2489
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Jing Liu102.70
Yue Wang277.63
Lihua Huang320315.20
Chenghong Zhang411618.03
Songzheng Zhao500.68