Home > Published Issues > 2023 > Volume 14, No. 3, 2023 >
JAIT 2023 Vol.14(3): 543-549
doi: 10.12720/jait.14.3.543-549

Utilizing Word Index Approach with LSTM Architecture for Extracting Adverse Drug Reaction from Medical Reviews

Asmaa J. M. Alshaikhdeeb * and Yu-N Cheah
School of Computer Sciences, Universiti Sains Malaysia, 11800, Penang, Malaysia; Email: yncheah@usm.my (Y.N.C.)
*Correspondence: ashaikhdeeb@student.usm.my (A.J.M.A.)

Manuscript received August 30, 2022; revised October 26, 2022; accepted December 1, 2022; published June 16, 2023.

Abstract—Adverse Drug Reaction (ADR) detection from social reviews refers to the task of exploring medical online stores and social reviews for extracting any mention of abnormal reactions that occur after consuming a particular medical product by the consumers themselves. A variety of approaches have been used for extracting ADR from social/medical reviews. These approaches include machine learning, dictionary-based and statistical approaches. Yet, these approaches showed either a high dependency on using an external knowledge source for ADR detection or relying on domain-dependent mechanisms that might lose contextual information. This study aims to propose word sequencing with Long Short-Term Memory (LSTM) architecture. A benchmark dataset of MedSyn has been used in the experiments. Then, a word indexing, mapping, and padding method have been used to represent the words within the reviews as fixed sequences. Such sequences have been fed into the LSTM consequentially. Experimental results showed that the proposed LSTM could achieve an F1 score of up to 92%. Comparing such a finding to the baseline studies reveals the superiority of LSTM. The demonstration of the efficacy of the proposed method has taken different forms including the examination of word indexing with different classifiers, the examination of different features with LSTM, and through the comparison against the baseline studies.
Keywords—adverse drug reaction extraction, word, mapping, word indexing, word embedding, long short-term memory

Cite: Asmaa J. M. Alshaikhdeeb and Yu-N Cheah, "Utilizing Word Index Approach with LSTM Architecture for Extracting Adverse Drug Reaction from Medical Reviews," Journal of Advances in Information Technology, Vol. 14, No. 3, pp. 543-549, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.