# Doctor Recommendation in Online Health Forums via Expertise Learning

arXiv: https://arxiv.org/abs/2203.02932 · HTML: https://mlsanity.com/paper/2203.02932/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 6 Mar 2022.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [K-BERT: Enabling Language Representation with Knowledge Graph](https://mlsanity.com/paper/1909.07606/) (2019)
- [MedDialog: Two Large-scale Medical Dialogue Datasets](https://mlsanity.com/paper/2004.03329/) (2020)
- [emrQA: A Large Corpus for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1809.00732/) (2018)
- [Parallel Data Augmentation for Formality Style Transfer](https://mlsanity.com/paper/2005.07522/) (2020)
- [Extracting Symptoms and their Status from Clinical Conversations](https://mlsanity.com/paper/1906.02239/) (2019)
- [Conceptualized Representation Learning for Chinese Biomedical Text Mining](https://mlsanity.com/paper/2008.10813/) (2020)
- [Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation](https://mlsanity.com/paper/2110.03996/) (2021)
- [PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity](https://mlsanity.com/paper/2106.01300/) (2021)
- [Deep Learning-based Online Alternative Product Recommendations at Scale](https://mlsanity.com/paper/2104.07572/) (2021)
- [DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain](https://mlsanity.com/paper/1906.04382/) (2019)
- [A Survey on Expert Recommendation in Community Question Answering](https://mlsanity.com/paper/1807.05540/) (2018)

## Built on by (most cited first, 1 of 1)

- [Prompt or Policy? Optimizing Physician Slate Presentations with LLM User Simulators](https://mlsanity.com/paper/2610.18309/) (2026)

## Abstract (arXiv)

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.