Doctor recommendation on healthcare consultation platforms: an integrated framework of knowledge graph and deep learning

Abstract

Purpose

Recommending suitable doctors to patients on healthcare consultation platforms is important to both the patients and the platforms. Although doctor recommendation methods have been proposed, they failed to explain recommendations and address the data sparsity problem, i.e. most patients on the platforms are new and provide little information except disease descriptions. This research aims to develop an interpretable doctor recommendation method based on knowledge graph and interpretable deep learning techniques to fill the research gaps.

Design/methodology/approach

This research proposes an advanced doctor recommendation method that leverages a health knowledge graph to overcome the data sparsity problem and uses deep learning techniques to generate accurate and interpretable recommendations. The proposed method extracts interactive features from the knowledge graph to indicate implicit interactions between patients and doctors and identifies individual features that signal the doctors' service quality. Then, the authors feed the features into a deep neural network with layer-wise relevance propagation to generate readily usable and interpretable recommendation results.

Findings

The proposed method produces more accurate recommendations than diverse baseline methods and can provide interpretations for the recommendations.

Originality/value

This study proposes a novel doctor recommendation method. Experimental results demonstrate the effectiveness and robustness of the method in generating accurate and interpretable recommendations. The research provides a practical solution and some managerial implications to online platforms that confront information overload and transparency issues.

Citation

Yuan, H. and Deng, W. (2022), "Doctor recommendation on healthcare consultation platforms: an integrated framework of knowledge graph and deep learning", Internet Research, Vol. 32 No. 2, pp. 454-476. https://doi.org/10.1108/INTR-07-2020-0379