论文标题
通过特定于域的预处理和伯特连接来检测来自Twitter的不良药物反应
Detecting Adverse Drug Reactions from Twitter through Domain-Specific Preprocessing and BERT Ensembling
论文作者
论文摘要
社交媒体中不良药物反应（ADR）检测的自动化将彻底改变药物保护的实践，支持药物调节剂，制药行业和公众，以确保日常实践中规定的药物的安全性。根据2019年8月的社交媒体挖掘（SMM4H）应用程序研讨会和共享任务的发表程序，我们旨在开发一种深度学习模型，以在包含药物提及的Twitter推文中对ADR进行分类。我们的方法涉及微调$ bert_ {大} $和两个特定领域的BERT实现，$ biobert $和$ bio + Clinicalbert $，应用了特定领域的预处理器，并开发了最大预测的结合方法。我们的最终模型导致在$ f_1 $ -score（0.6681）和召回（0.7700）上的最先进性能优于2019年SMM4H中提交的所有型号以及迄今为止评估后的所有模型。
The automation of adverse drug reaction (ADR) detection in social media would revolutionize the practice of pharmacovigilance, supporting drug regulators, the pharmaceutical industry and the general public in ensuring the safety of the drugs prescribed in daily practice. Following from the published proceedings of the Social Media Mining for Health (SMM4H) Applications Workshop & Shared Task in August 2019, we aimed to develop a deep learning model to classify ADRs within Twitter tweets that contain drug mentions. Our approach involved fine-tuning $BERT_{LARGE}$ and two domain-specific BERT implementations, $BioBERT$ and $Bio + clinicalBERT$, applying a domain-specific preprocessor, and developing a max-prediction ensembling approach. Our final model resulted in state-of-the-art performance on both $F_1$-score (0.6681) and recall (0.7700) outperforming all models submitted in SMM4H 2019 and during post-evaluation to date.
