论文标题
“我的鼻子在奔跑。”“你也咳嗽吗？”：建立具有可解释的查询逻辑的医学诊断剂
"My nose is running.""Are you also coughing?": Building A Medical Diagnosis Agent with Interpretable Inquiry Logics
论文作者
论文摘要
随着远程医疗的兴起，近年来，开发医学诊断对话系统（DSMD）的任务受到了很多关注。与需要依靠额外的人力资源和专业知识来帮助构建系统的早期研究不同，最近的研究重点是如何以纯粹的数据驱动方式构建DSMD。但是，以前的数据驱动的DSMD方法在很大程度上忽略了系统的可解释性，这对于医疗应用至关重要，它们也同时遇到了数据稀少问题。在本文中，我们探讨了如何为数据驱动的DSMD带来可解释性。具体而言，我们提出了一个更容易解释的决策过程，通过合理地模仿真正的医生的询问逻辑来实施DSMD的对话经理，并设计了一个具有高度透明组件来进行推理的模型。此外，我们收集了一个新的DSMD数据集，该数据集具有更大的规模，更多样化的模式，并且质量高于现有模式。实验表明，我们的方法在三个数据集上分别获得了7.7％，10.0％，3.0％的绝对诊断准确性提高，这证明了其合理决策过程和模型设计的有效性。我们的代码和GMD-12数据集可在https://github.com/lwgkzl/br-agent上找到。
With the rise of telemedicine, the task of developing Dialogue Systems for Medical Diagnosis (DSMD) has received much attention in recent years. Different from early researches that needed to rely on extra human resources and expertise to help construct the system, recent researches focused on how to build DSMD in a purely data-driven manner. However, the previous data-driven DSMD methods largely overlooked the system interpretability, which is critical for a medical application, and they also suffered from the data sparsity issue at the same time. In this paper, we explore how to bring interpretability to data-driven DSMD. Specifically, we propose a more interpretable decision process to implement the dialogue manager of DSMD by reasonably mimicking real doctors' inquiry logics, and we devise a model with highly transparent components to conduct the inference. Moreover, we collect a new DSMD dataset, which has a much larger scale, more diverse patterns and is of higher quality than the existing ones. The experiments show that our method obtains 7.7%, 10.0%, 3.0% absolute improvement in diagnosis accuracy respectively on three datasets, demonstrating the effectiveness of its rational decision process and model design. Our codes and the GMD-12 dataset are available at https://github.com/lwgkzl/BR-Agent.
