Two Step Joint Model for Drug Drug Interaction Extraction

• URL: http://arxiv.org/abs/2008.12704v1
• Date: Fri, 28 Aug 2020 15:30:08 GMT
• Title: Two Step Joint Model for Drug Drug Interaction Extraction
• Authors: Siliang Tang, Qi Zhang, Tianpeng Zheng, Mengdi Zhou, Zhan Chen, Lixing Shen, Xiang Ren, Yueting Zhuang, Shiliang Pu and Fei Wu
• Abstract summary: Drug-Drug Interaction (DDI) Extraction from Drug Labels challenge of Text Analysis Conference (TAC) 2018. We propose a two step joint model to detect DDI and it's related mentions jointly. A sequence tagging system (CNN-GRU encoder-decoder) finds precipitants first and search its fine-grained Trigger and determine the DDI for each precipitant in the second step.
• Score: 82.49278654043577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When patients need to take medicine, particularly taking more than one kind of drug simultaneously, they should be alarmed that there possibly exists drug-drug interaction. Interaction between drugs may have a negative impact on patients or even cause death. Generally, drugs that conflict with a specific drug (or label drug) are usually described in its drug label or package insert. Since more and more new drug products come into the market, it is difficult to collect such information by manual. We take part in the Drug-Drug Interaction (DDI) Extraction from Drug Labels challenge of Text Analysis Conference (TAC) 2018, choosing task1 and task2 to automatically extract DDI related mentions and DDI relations respectively. Instead of regarding task1 as named entity recognition (NER) task and regarding task2 as relation extraction (RE) task then solving it in a pipeline, we propose a two step joint model to detect DDI and it's related mentions jointly. A sequence tagging system (CNN-GRU encoder-decoder) finds precipitants first and search its fine-grained Trigger and determine the DDI for each precipitant in the second step. Moreover, a rule based model is built to determine the sub-type for pharmacokinetic interation. Our system achieved best result in both task1 and task2. F-measure reaches 0.46 in task1 and 0.40 in task2.

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