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.
%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 l3s 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 = {2022-02-18T16:29:02.000+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/khosla},
booktitle = {Proceedings of the 30th ACM International Conference on Information & Knowledge Management},
interhash = {99856715f3ed6546585345a26eed6cd4},
intrahash = {049aa8cbd0ff02b4bb28eb7fe7bfbbf8},
isbn = {9781450384469},
keywords = {l3s leibnizailab myown},
numpages = {5},
pages = {3398–3402},
publisher = {Association for Computing Machinery},
timestamp = {2022-02-18T16:29:02.000+0100},
title = {Knowledge-Aware Neural Networks for Medical Forum Question Classification},
url = {https://doi.org/10.1145/3459637.3482128},
year = 2021
}