  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.
