Online medical forums have become a predominant platform for answering health-related information needs of consumers. However, with a significant rise in the number of queries and the limited availability of experts, it is necessary to automatically classify medical queries based on a consumer's intention, so that these questions may be directed to the right set of medical experts. Here, we develop a novel medical knowledge-aware BERT-based model (MedBERT) that explicitly gives more weightage to medical concept-bearing words, and utilize domain-specific side information obtained from a popular medical knowledge base. We also contribute a multi-label dataset for the Medical Forum Question Classification (MFQC) task. MedBERT achieves state-of-the-art performance on two benchmark datasets and performs very well in low resource settings.
翻译:在线医疗论坛已成为满足消费者健康相关信息需求的主要平台,然而,随着问询数量大幅增加和专家数量有限,有必要根据消费者的意向对医疗询问进行自动分类,以便这些问题可以针对正确的医疗专家组。 在这里,我们开发了一种新的医学知识意识BERT模型(MedBERT),明确赋予医学概念字词以更多的分量,并利用从一个广受欢迎的医疗知识库获得的域别侧面信息。 我们还为医学论坛问题分类(MFQC)任务贡献了多标签数据集。 MedBERT在两个基准数据集上取得了最先进的业绩,并在低资源环境中表现良好。