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DeepCADRME: A deep neural model for complex adverse drug reaction mentions extraction

机译：Deepcadrme：复杂不良药物反应提升的深度神经模型

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摘要

Extracting mentions of Adverse Drug Reaction (ADR) from biomedical texts, aiming to support pharmacovigilance and drug safety surveillance, remains a challenging task as many ADR mentions are nested, discontinuous and overlapping. To solve these issues, in this paper, we propose a deep neural model for Complex Adverse Drug Reaction Mentions Extraction, called DeepCADRME. It first transforms the ADR mentions extraction problem as an N-level tagging sequence. Then, it feeds the sequences to an N level model based on contextual embeddings where the output of the pre-trained model of the current level is used to build a new deep contextualized representation for the next level. This allows the DeepCADRME system to transfer knowledge between levels. Experimental results performed on the TAC 2017 ADR dataset, show the effectiveness of DeepCADRME which leads to a new state-of-the-art performance by reaching a F1 of 85.35% and 85.41% with and without mention types, respectively. The evaluation results also highlight the benefits of exploring language model to effectively extract different types of ADR mentions. (c) 2020 Elsevier B.V. All rights reserved.
机译：从生物医学文本中提取不良药物反应（ADR）的提及，旨在支持药物检测和药物安全监测，仍然是一个具有挑战性的任务，因为许多ADR提及嵌套，不连续和重叠。为了解决这些问题，在本文中，我们提出了一种深深的神经模型，用于复杂不良药物反应提取提取，称为Deepcadrme。首先将ADR提取提取问题转换为N级标记序列。然后，它将序列基于基于上下文嵌入的N级模型，其中当前级别的预先训练模型的输出用于为下一个级别构建新的深层上下文化表示。这允许Deepcadrme系统在级别之间传输知识。对TAC 2017年ADR数据集进行的实验结果表明，Deepcadrme的有效性，通过分别达到85.35％和85.41％的F1，分别与且不提及类型的新的最先进的性能。评估结果还突出了探索语言模型，以有效提取不同类型的ADR提升的益处。 （c）2020 Elsevier B.v.保留所有权利。

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