A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events

Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3414–3423 November 16 - 20, 2020. c⃝2020 Association for Computational Linguistics 3414 A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events Susmitha Wunnava Worcester Polytechnic Institute 100 Institute Rd Worcester, MA 01609 swunnava@wpi.edu Xiao Qin IBM Research - Almaden 650 Harry Road San Jose CA 95120 xiao.qin@ibm.com Tabassum Kakar Worcester Polytechnic Institute 100 Institute Rd Worcester, MA 01609 tkakar@wpi.edu Xiangnan Kong Worcester Polytechnic Institute 100 Institute Rd Worcester, MA 01609 xkong@wpi.edu Elke A. Rundensteiner Worcester Polytechnic Institute 100 Institute Rd Worcester, MA 01609 rundenst@wpi.edu Abstract An adverse drug event (ADE) is an injury re- sulting from medical intervention related to a drug. ADE detection from text can be ei- ther fine-grained (ADE entity recognition) or coarse-grained (ADE assertive sentence classi- fication), with limited efforts leveraging inter- dependencies among these two granularities. We instead design a multi-grained joint deep network model MGADE to concurrently solve both ADE tasks MGADE takes advantage of their symbiotic relationship, with a transfer of knowledge between the two levels of granular- ity. Our dual-attention mechanism constructs multiple distinct representations of a sentence that capture both task-specific and semantic in- formation in the sentence, providing stronger emphasis on the key elements essential for sen- tence classification. Our model improves state- of-art F1-score for both tasks: (i) entity recog- nition of ADE words (12.5% increase) and (ii) ADE sentence classification (13.6% increase) on MADE 1.0 benchmark of EHR notes. 1 Introduction Background. Adverse drug events (ADEs), in- juries resulting from medical intervention, are a leading cause of death in the United States and cost around $30˜$130 billion every year (Donaldson et al., 2000). Early detection of ADE incidents aids in the timely assessment, mitigation and preven- tion of future occurrences of ADEs. Natural Lan- guage Processing techniques have been recognized as instrumental in identifying ADEs and related information from unstructured text fields of sponta- neous reports and electronic health records (EHRs) and thus in improving drug safety monitoring and pharmacovigilance (Harpaz et al., 2014). Fine-grained ADE detection identifies named ADE entities at the word-level, while coarse- grained ADE detection (also ADE assertive text classification) identifies complete sentences de- scribing drug-related adverse effects. (Gurulin- gappa et al., 2011)’s system for identification of ADE assertive sentences in medical case reports targets the important application of detecting under- reported and under-documented adverse drug ef- fects. Lastly, multi-grained ADE detection identi- fies ADE information at multiple levels of granu- larity, namely, both entity and sentence level. As example, Figure 1 displays ADE and non- ADE sentences. The first is an ADE sentence where the mentions of Drugname and ADE entities have the appropriate relationship with each other. Sec- ond and third sentences show that the mention of an ADE entity by itself is not sufficient to assert a drug-related adverse side effect. Recently, deep learning-based sequence ap- proaches have shown some promise in extracting fine-grained ADEs and related named entities from text (Liu et al., 2019). …

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