论文标题
通过专业知识学习在线健康论坛中的医生推荐
Doctor Recommendation in Online Health Forums via Expertise Learning
论文作者
论文摘要
每天在在线健康论坛上产生大量的患者查询，使手动医生分配一项劳动密集型任务。为了更好地帮助患者，本文研究了医生建议的一项新任务，以使患者与具有相关专业知识的医生自动配对。尽管建议大多数先前的工作都集中在对目标用户过去的行为中进行建模，但由于隐私原因，我们只能依靠查询中的有限单词来推断患者的需求。对于医生建模，我们研究了他们的概况和先前与其他患者对话的共同影响，并通过自学探索他们的相互作用。学习的医生嵌入将进一步使用，以估计其通过多头注意机制处理患者查询的能力。对于实验，从中国在线健康论坛Chunyu Yisheng收集了一个大规模数据集，我们的模型在该论坛上展示了最先进的结果，优于基本的基线仅考虑配置文件和过去的对话来表征医生。
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.
