Doctor Recommendation in Online Health Forums via Expertise Learning (2022)

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.

## introduction the growing popularity of health communities on social media has revolutionized the traditional doctor consultancy paradigm in a face-to-face manner. massive amounts of patients are now turning to online health forums to seek professional help; meanwhile, popular healthcare platforms are able to recruit a large group of licensed doctors to provide online service @xcite . in the covid-19 crisis, the social distancing policies further flourish the use of these forums, where numerous patients would query diverse varieties of health problems every day @xcite . ## data collection and analysis despite the previous contributions of large-scale data with doctor-patient dialogues @xcite , we note some essential information for doctor modeling is missing, e.g., the profiles. in this work, we present a new dataset to study the characterization of doctor expertise on health forums from both profiles and dialogue history. ## doctor recommendation framework we now introduce the proposed framework for our doctor recommendation task (overviewed in figure 3 ). it contains three modules: a query encoder that encodes patient needs from queries, a doctor encoder that encodes doctor expertise from profiles and dialogues, and a prediction layer that couples above outputs for recommendation prediction. model's input and output. the input of our model is from three sources: a query q from a patient, the profile p i of doctor d i , and a collection of d i 's history dialogues d i 1 , d i 2 , ..., d in (i n denotes the number of dialogues d i previously engaged). for each given query q, we first pair it with each doctor d i from a candidate pool of m doctors and output a matching score s i to reflect how likely d i owns the expertise to handle the request of q. a recommendation is then made for q by ranking all the doctor candidates based on these matching scores s i (@xmath0, ..., m}). ## experimental setup we now describe the set up for our experiments. ## experimental results in this section, we first present the main comparison results in section 5.1. then, we quantify the model sensitivity to queries, profiles, and dialogues in varying lengths in section 5.2. finally, section 5.3 analyzes the effects of head number in validation performance, followed by a case study to interpret our superiority and error analysis to provide insights to future work. ## related work our work is in the research line of recommender systems widely studied because of their practical value in industry @xcite . for exam-ple, previous work explores users' chatting history to recommend conversations @xcite and hashtags @xcite , browsing history to recommend news @xcite , and purchase history to recommend products @xcite . in contrast to most recommendation studies focusing on exploiting target users' personal interest modeling from their history behavior, our work largely relies on wordings of a short query to figure out what is needed by a target user (patient) because they are anonymous for privacy concern. within several branches of recommendation research, our task is by concept similar to expert recommendation for question answering @xcite . in this field, many previous studies encode expertise knowledge in diverse streams, such as software engineering @xcite , social activities @xcite , etc. nevertheless, few of them attempt to model expertise with nlp methods. on the contrary, language representations play an important role here to tackle our task: we substantially explore how semantic features help characterize doctor expertise, which has not been studied before. our work is also related to the previous language understanding research over doctor-patient dialogues on online health forums @xcite , where various compelling applications are explored, such as information extraction @xcite @xcite , question answering @xcite , and medical report generation @xcite . in comparison with them, we concern doctor expertise and characterize it from both doctor profiles and the past patient-doctor dialogues, which is a gap in previous work filled in this work. ## conclusion this paper has studied doctor recommendation in online health forums. we have explored the effects of doctor profiles and history dialogues in the learning of doctor expertise through a self-learning task and a multi-head attention mechanism. substantial experiments on a large-scale chinese dataset demonstrate the effectiveness of our method.