Doctor Recommendation in Online Health Forums via Expertise Learning

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 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 selflearning. The learned doctor embeddings are further employed to estimate their capabilities of handling a patient query with a multihead attention mechanism. For experiments, a large-scale dataset is collected from Chunyu Yisheng, a Chinese online health forum, where our model exhibits state-of-the-art results, outperforming baselines only considering profiles and past dialogues to characterize a doctor. 1