%0 Book Section %1 10.1145/3459637.3482128 %A Roy, Soumyadeep %A Chakraborty, Sudip %A Mandal, Aishik %A Balde, Gunjan %A Sharma, Prakhar %A Natarajan, Anandhavelu %A Khosla, Megha %A Sural, Shamik %A Ganguly, Niloy %B Proceedings of the 30th ACM International Conference on Information & Knowledge Management %C New York, NY, USA %D 2021 %I Association for Computing Machinery %K leibnizailab myown %P 3398–3402 %T Knowledge-Aware Neural Networks for Medical Forum Question Classification %U https://doi.org/10.1145/3459637.3482128 %X 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. %@ 9781450384469

@inbook{10.1145/3459637.3482128, abstract = {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.}, added-at = {2022-02-19T19:41:18.000+0100}, address = {New York, NY, USA}, author = {Roy, Soumyadeep and Chakraborty, Sudip and Mandal, Aishik and Balde, Gunjan and Sharma, Prakhar and Natarajan, Anandhavelu and Khosla, Megha and Sural, Shamik and Ganguly, Niloy}, biburl = {https://www.bibsonomy.org/bibtex/2049aa8cbd0ff02b4bb28eb7fe7bfbbf8/niloy}, booktitle = {Proceedings of the 30th ACM International Conference on Information & Knowledge Management}, interhash = {99856715f3ed6546585345a26eed6cd4}, intrahash = {049aa8cbd0ff02b4bb28eb7fe7bfbbf8}, isbn = {9781450384469}, keywords = {leibnizailab myown}, numpages = {5}, pages = {3398–3402}, publisher = {Association for Computing Machinery}, timestamp = {2022-02-25T09:03:01.000+0100}, title = {Knowledge-Aware Neural Networks for Medical Forum Question Classification}, url = {https://doi.org/10.1145/3459637.3482128}, year = 2021 }