Doctor Recommendation in Online Health Forums via Expertise Learning
It addresses the labor-intensive task of manual doctor allocation for patients in online health forums, though it is incremental as it builds on prior recommendation methods.
This paper tackles the problem of automatically recommending doctors to patients in online health forums by learning doctor expertise from profiles and dialogues, achieving state-of-the-art results on a large-scale Chinese dataset.
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.