ArticleBMC bioinformatics2019
Adverse drug reaction detection via a multihop self-attention mechanism.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- A Fusion Deep Learning Model for Predicting Adverse Drug Reactions Based on Multiple Drug Characteristics.Life (Basel, Switzerland) · 2025Article
- Predicting Drugs Suspected of Causing Adverse Drug Reactions Using Graph Features and Attention Mechanisms.Pharmaceuticals (Basel, Switzerland) · 2024Article
- iADRGSE: A Graph-Embedding and Self-Attention Encoding for Identifying Adverse Drug Reaction in the Earlier Phase of Drug Development.International journal of molecular sciences · 2022Article
- Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review.Drug safety · 2022Article
- Pharmacovigilance with Transformers: A Framework to Detect Adverse Drug Reactions Using BERT Fine-Tuned with FARM.Computational and mathematical methods in medicine · 2021Article
- Prospective Evaluation of Adverse Event Recognition Systems in Twitter: Results from the Web-RADR Project.Drug safety · 2020Article
- Adverse Drug Reaction Discovery Using a Tumor-Biomarker Knowledge Graph.Frontiers in genetics · 2020Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe adverse reactions that are caused by drugs are potentially life-threatening problems. Comprehensive knowledge of adverse drug reactions (ADRs) can reduce their detrimental impacts on patients. Detecting ADRs through clinical trials takes a large number of experiments and a long period of time. With the growing amount of unstructured textual data, such as biomedical literature and electronic records, detecting ADRs in the available unstructured data has important implications for ADR research. Most of the neural network-based methods typically focus on the simple semantic information of sentence sequences; however, the relationship of the two entities depends on more complex semantic information.
methodsIn this paper, we propose multihop self-attention mechanism (MSAM) model that aims to learn the multi-aspect semantic information for the ADR detection task. first, the contextual information of the sentence is captured by using the bidirectional long short-term memory (Bi-LSTM) model. Then, via applying the multiple steps of an attention mechanism, multiple semantic representations of a sentence are generated. Each attention step obtains a different attention distribution focusing on the different segments of the sentence. Meanwhile, our model locates and enhances various keywords from the multiple representations of a sentence.
resultsOur model was evaluated by using two ADR corpora. It is shown that the method has a stable generalization ability. Via extensive experiments, our model achieved F-measure of 0.853, 0.799 and 0.851 for ADR detection for TwiMed-PubMed, TwiMed-Twitter, and ADE, respectively. The experimental results showed that our model significantly outperforms other compared models for ADR detection.
conclusionsIn this paper, we propose a modification of multihop self-attention mechanism (MSAM) model for an ADR detection task. The proposed method significantly improved the learning of the complex semantic information of sentences.
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