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With the rise of telemedicine, the task of developing Dialogue Systems forMedical Diagnosis (DSMD) has received much attention in recent years. Differentfrom early researches that needed to rely on extra human resources andexpertise to help construct the system, recent researches focused on how tobuild DSMD in a purely data-driven manner. However, the previous data-drivenDSMD methods largely overlooked the system interpretability, which is criticalfor a medical application, and they also suffered from the data sparsity issueat the same time. In this paper, we explore how to bring interpretability todata-driven DSMD. Specifically, we propose a more interpretable decisionprocess to implement the dialogue manager of DSMD by reasonably mimicking realdoctors' inquiry logics, and we devise a model with highly transparentcomponents to conduct the inference. Moreover, we collect a new DSMD dataset,which has a much larger scale, more diverse patterns and is of higher qualitythan the existing ones. The experiments show that our method obtains 7.7%,10.0%, 3.0% absolute improvement in diagnosis accuracy respectively on threedatasets, demonstrating the effectiveness of its rational decision process andmodel design. Our codes and the GMD-12 dataset are available athttps://github.com/lwgkzl/BR-Agent.

"My nose is running.""Are you also coughing?": Building A Medical Diagnosis Agent with Interpretable Inquiry Logics