In this paper, we focus on the classification of tweets as sources of
potential signals for adverse drug effects (ADEs) or drug reactions (ADRs).
Following the intuition that text and drug structure representations are
complementary, we introduce a multimodal model with two components. These
components are state-of-the-art BERT-based models for language understanding
and molecular property prediction. Experiments were carried out on multilingual
benchmarks of the Social Media Mining for Health Research and Applications
(#SMM4H) initiative. Our models obtained state-of-the-art results of 0.61 F1
and 0.57 F1 on #SMM4H 2021 Shared Tasks 1a and 2 in English and Russian,
respectively. On the classification of French tweets from SMM4H 2020 Task 1,
our approach pushes the state of the art by an absolute gain of 8% F1. Our
experiments show that the molecular information obtained from neural networks
is more beneficial for ADE classification than traditional molecular
descriptors. The source code for our models is freely available at
this https URL.

本文介绍了一种基于多模态模型和 BERT 的方法，用于对推文进行分类以检测潜在的药物效应和反应。实验证明，神经网络得到的分子信息对 ADE 分类比传统分子描述符更有益。