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Position-aware deep multi-task learning for drug–drug interaction extraction
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Zhou, Deyu, Miao, Lei and He, Yulan (2018) Position-aware deep multi-task learning for drug–drug interaction extraction. Artificial Intelligence in Medicine, 87 . pp. 1-8. doi:10.1016/j.artmed.2018.03.001 ISSN 0933-3657.
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Official URL: http://dx.doi.org/10.1016/j.artmed.2018.03.001
Abstract
Objective
A drug–drug interaction (DDI) is a situation in which a drug affects the activity of another drug synergistically or antagonistically when being administered together. The information of DDIs is crucial for healthcare professionals to prevent adverse drug events. Although some known DDIs can be found in purposely-built databases such as DrugBank, most information is still buried in scientific publications. Therefore, automatically extracting DDIs from biomedical texts is sorely needed.
Methods and material
In this paper, we propose a novel position-aware deep multi-task learning approach for extracting DDIs from biomedical texts. In particular, sentences are represented as a sequence of word embeddings and position embeddings. An attention-based bidirectional long short-term memory (BiLSTM) network is used to encode each sentence. The relative position information of words with the target drugs in text is combined with the hidden states of BiLSTM to generate the position-aware attention weights. Moreover, the tasks of predicting whether or not two drugs interact with each other and further distinguishing the types of interactions are learned jointly in multi-task learning framework.
Results
The proposed approach has been evaluated on the DDIExtraction challenge 2013 corpus and the results show that with the position-aware attention only, our proposed approach outperforms the state-of-the-art method by 0.99% for binary DDI classification, and with both position-aware attention and multi-task learning, our approach achieves a micro F-score of 72.99% on interaction type identification, outperforming the state-of-the-art approach by 1.51%, which demonstrates the effectiveness of the proposed approach.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RM Therapeutics. Pharmacology |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Drug interactions -- Software, Drug synergism -- Software, Drug antagonism -- Software | |||||||||||||||
Journal or Publication Title: | Artificial Intelligence in Medicine | |||||||||||||||
Publisher: | Elsevier BV | |||||||||||||||
ISSN: | 0933-3657 | |||||||||||||||
Official Date: | May 2018 | |||||||||||||||
Dates: |
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Volume: | 87 | |||||||||||||||
Page Range: | pp. 1-8 | |||||||||||||||
DOI: | 10.1016/j.artmed.2018.03.001 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
Date of first compliant deposit: | 29 September 2018 | |||||||||||||||
Date of first compliant Open Access: | 17 March 2019 | |||||||||||||||
RIOXX Funder/Project Grant: |
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