Abstract
Huge volumes of patient queries are daily generated on online health forums, rendering manual doctor allocation a labor-intensive task. To better help patients, this paper studies a novel task of doctor recommendation to enable automatic pairing of a patient to a doctor with relevant expertise. While most prior work in recommendation focuses on modeling target users from their past behavior, we can only rely on the limited words in a query to infer a patient’s needs for privacy reasons. For doctor modeling, we study the joint effects of their profiles and previous dialogues with other patients and explore their interactions via self-learning. The learned doctor embeddings are further employed to estimate their capabilities of handling a patient query with a multi-head attention mechanism. For experiments, a large-scale dataset is collected from Chunyu Yisheng, a Chinese online health forum, where our model exhibits the state-of-the-art results, outperforming baselines only consider profiles and past dialogues to characterize a doctor.- Anthology ID:
- 2022.acl-long.79
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1111–1123
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.79
- DOI:
- 10.18653/v1/2022.acl-long.79
- Cite (ACL):
- Xiaoxin Lu, Yubo Zhang, Jing Li, and Shi Zong. 2022. Doctor Recommendation in Online Health Forums via Expertise Learning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1111–1123, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Doctor Recommendation in Online Health Forums via Expertise Learning (Lu et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.acl-long.79.pdf
- Code
- polyusmart/doctor-recommendation