Exploring Joint AB-LSTM With Embedded Lemmas for Adverse Drug Reaction Discovery

• Published in: IEEE journal of biomedical and health informatics Vol. 23; no. 5; pp. 2148 - 2155
• Main Authors: Santiso, Sara, Perez, Alicia, Casillas, Arantza
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-09-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adverse drug reactions; Algorithms; Artificial neural networks; Data Mining - methods; Deep Learning; deep neural networks; Diseases; Drug-Related Side Effects and Adverse Reactions - classification; Drugs; Electronic Health Records; Electronic medical records; Feature extraction; Humans; Informatics; Long short-term memory; Medical Informatics - methods; Neural networks; Representations; Side effects; Skewed distributions; Task analysis; Text mining; Variability; Workload
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2018.2879744

This work focuses on the detection of adverse drug reactions (ADRs) in electronic health records (EHRs) written in Spanish. The World Health Organization underlines the importance of reporting ADRs for patients' safety. The fact is that ADRs tend to be under-reported in daily hospital praxis. In this context, automatic solutions based on text mining can help to alleviate the workload of experts. Nevertheless, these solutions pose two challenges: 1) EHRs show high lexical variability, the characterization of the events must be able to deal with unseen words or contexts and 2) ADRs are rare events, hence, the system should be robust against skewed class distribution. To tackle these challenges, deep neural networks seem appropriate because they allow a high-level representation. Specifically, we opted for a joint AB-LSTM network, a sub-class of the bidirectional long short-term memory network. Besides, in an attempt to reinforce lexical variability, we proposed the use of embeddings created using lemmas. We compared this approach with supervised event extraction approaches based on either symbolic or dense representations. Experimental results showed that the joint AB-LSTM approach outperformed previous approaches, achieving an f-measure of 73.3.