Abstract
Preventable adverse drug reactions as a result of medical errors present a growing concern in modern medicine. As drug-drug interactions (DDIs) may cause adverse reactions, being able to extracting DDIs from drug labels into machine-readable form is an important effort in effectively deploying drug safety information. The DDI track of TAC 2018 introduces two large hand-annotated test sets for the task of extracting DDIs from structured product labels with linkage to standard terminologies. Herein, we describe our approach to tackling tasks one and two of the DDI track, which corresponds to named entity recognition (NER) and sentence-level relation extraction respectively. Namely, our approach resembles a multi-task learning framework designed to jointly model various sub-tasks including NER and interaction type and outcome prediction. On NER, our system ranked second (among eight teams) at 33.00% and 38.25% F1 on Test Sets 1 and 2 respectively. On relation extraction, our system ranked second (among four teams) at 21.59% and 23.55% on Test Sets 1 and 2 respectively.
Abstract (translated)
可预防的药物不良反应，由于医疗差错目前日益关注现代医学。由于药物相互作用（DDI）可能引起不良反应，从药品标签中提取DDI到机器可读形式是有效部署药品安全信息的一项重要工作。TAC 2018的DDI跟踪引入了两个大的手工注释测试集，用于从结构化产品标签中提取DDI，并将其链接到标准术语。在此，我们描述了处理DDI轨迹中任务1和任务2的方法，分别对应于命名实体识别（NER）和句子级关系提取。也就是说，我们的方法类似于一个多任务学习框架，旨在联合建模各种子任务，包括NER和交互类型以及结果预测。在NER方面，我们的系统在测试集1和测试集2上分别以33.00%和38.25%的F1排名第二（在八个团队中）。在关系提取方面，我们的系统在测试集1和2上分别排名第二（在四个团队中），分别为21.59%和23.55%。
URL
https://arxiv.org/abs/1905.07464
