Doctor recommendation on healthcare consultation platforms

An integrated framework of knowledge graph and deep learning

Bibliographic Data

ID12420787
AuthorsYuan Hui (0000-0001-9267-8721, Shanghai International Studies University, corresponding author), Weiwei Deng (0000-0001-6589-2116, South China Normal University)
Year2021
Volume32
Issue2
Pages454-476
Publication date2021-06-08
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternet Research (JOURNAL)
Journal identifiersISSN: 1066-2243 • E-ISSN: 2054-5657
PublisherEmerald Publishing Limited (PUBLISHER • GB)
DOI10.1108/intr-07-2020-0379
OpenAlexW3167069287
LanguageEN
Citations received6
References cited40

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

Data science · Deep learning · Graph · Information overload · Information retrieval · Knowledge graph · Machine learning · Recommender system · Relevance (law · Robustness (evolution · USable · World Wide Web · Advanced Graph Neural Networks · Computer Science · Health Literacy and Information Accessibility · Recommender Systems and Techniques · Artificial Intelligence

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