Doctor Recommendation in Online Health Forums via Expertise Learning

TL;DR

This paper introduces a novel automatic doctor recommendation system for online health forums that leverages limited query words, doctor profiles, and dialogue history to improve matching accuracy with state-of-the-art results.

Contribution

It proposes a new approach combining expertise learning from profiles and dialogues with multi-head attention to enhance doctor-patient matching accuracy.

Findings

Model outperforms baselines on large-scale Chinese health forum data.

Expertise learning from multiple sources improves recommendation accuracy.

Multi-head attention effectively estimates doctor capabilities.

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.