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.

中文翻译：

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医疗咨询平台上的医生推荐：知识图谱与深度学习的集成框架

目的

在医疗咨询平台上为患者推荐合适的医生对患者和平台都很重要。尽管已经提出了医生推荐方法，但它们未能解释推荐并解决数据稀疏问题，即平台上的大多数患者都是新的并且除了疾病描述之外提供的信息很少。本研究旨在开发一种基于知识图谱和可解释深度学习技术的可解释医生推荐方法，以填补研究空白​​。

设计/方法/方法

本研究提出了一种先进的医生推荐方法，该方法利用健康知识图来克服数据稀疏问题，并使用深度学习技术生成准确且可解释的推荐。所提出的方法从知识图中提取交互特征以指示患者和医生之间的隐式交互，并识别表明医生服务质量的个体特征。然后，作者将这些特征输入到具有分层相关性传播的深度神经网络中，以生成易于使用和可解释的推荐结果。

发现

所提出的方法比各种基线方法产生更准确的推荐，并且可以为推荐提供解释。

原创性/价值

本研究提出了一种新颖的医生推荐方法。实验结果证明了该方法在生成准确和可解释的推荐方面的有效性和鲁棒性。该研究为面临信息过载和透明度问题的在线平台提供了实用的解决方案和一些管理启示。