Xiong, S, Batra, V, Liu, L, Xi, L and Sun, C (2022) Detecting Personal Medication Intake in Twitter via Domain Attention-Based RNN with Multi-Level Features. Computational Intelligence and Neuroscience, 2022. 1 - 7. ISSN 1687-5265

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<jats:p>Personal medication intake detection aims to automatically detect tweets that show clear evidence of personal medication consumption. It is a research topic that has attracted considerable attention to drug safety surveillance. This task is inevitably dependent on medical domain information, and the current main model for this task does not explicitly consider domain information. To tackle this problem, we propose a domain attention mechanism for recurrent neural networks, LSTMs, with a multi-level feature representation of Twitter data. Specifically, we utilize character-level CNN to capture morphological features at the word level. Subsequently, we feed them with word embeddings into a BiLSTM to get the hidden representation of a tweet. An attention mechanism is introduced over the hidden state of the BiLSTM to attend to special medical information. Finally, a classification is performed on the weighted hidden representation of tweets. Experiments over a publicly available benchmark dataset show that our model can exploit a domain attention mechanism to consider medical information to improve performance. For example, our approach achieves a precision score of 0.708, a recall score of 0.694, and a F1 score of 0.697, which is significantly outperforming multiple strong and relevant baselines.</jats:p>

Item Type: Article
Additional Information: Copyright © 2022 Shufeng Xiong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 11 Aug 2022 11:28
Last Modified: 11 Aug 2022 11:28
URI: https://eprints.keele.ac.uk/id/eprint/11248

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