Abstract

An experimental work on the analysis of effectiveness of neural network models applied to the classification of adverse drug reactions at the entity level is described. Aspect-level sentiment analysis, which aims to determine the sentimental class of a specific aspect conveyed in user opinions, has been actively studied for more than 10 years. A number of neural network architectures have been proposed. Even though the models based on these architectures have much in common, they differ in certain components. In this paper, the applicability of the neural network models developed for the aspect-level sentiment analysis to the problem of the classification of adverse drug reactions is studied. Extensive experiments on English language texts of biomedical topic, including health records, scientific literature, and social media have been conducted. The proposed models mentioned above are compared with one of the best model based on the support vector machine method and a large set of features.

中文翻译：

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药物不良反应的实体水平分类：神经网络模型的比较分析

摘要

描述了在实体级别对应用于不良药物反应分类的神经网络模型的有效性进行分析的实验工作。方面级别的情感分析旨在确定用户意见中传达的特定方面的情感类别，已经进行了十多年的积极研究。已经提出了许多神经网络架构。即使基于这些体系结构的模型有很多共同点，但它们在某些组件上也有所不同。本文研究了用于方面水平情感分析的神经网络模型在药物不良反应分类问题中的适用性。已经对生物医学主题的英语文本（包括健康记录，科学文献和社交媒体）进行了广泛的实验。