In this work, we propose a novel goal-oriented dialog task, automatic symptom detection. We build a system that can interact with patients through dialog to detect and collect clinical symptoms automatically, which can save a doctor's time interviewing the patient. Given a set of explicit symptoms provided by the patient to initiate a dialog for diagnosing, the system is trained to collect implicit symptoms by asking questions, in order to collect more information for making an accurate diagnosis. After getting the reply from the patient for each question, the system also decides whether current information is enough for a human doctor to make a diagnosis. To achieve this goal, we propose two neural models and a training pipeline for the multi-step reasoning task. We also build a knowledge graph as additional inputs to further improve model performance. Experiments show that our model significantly outperforms the baseline by 4%, discovering 67% of implicit symptoms on average with a limited number of questions.

翻译：在这项工作中,我们提出了一个新的面向目标的对话任务,即自动症状检测。我们建立了一个能够通过对话与病人互动的系统,以便自动检测和收集临床症状,这可以节省医生与病人面谈的时间。考虑到病人为启动诊断对话而提供的一组明确的症状,该系统经过培训,通过提问收集隐性症状,以便收集更多的信息进行准确诊断。在从病人那里得到每个问题的答复后,该系统还决定目前的信息是否足以让人类医生进行诊断。为了实现这一目标,我们提出了两个神经模型和一个多步骤推理任务的培训管道。我们还建立了一个知识图表,作为进一步改进模型性能的补充投入。实验显示,我们的模型大大超过基线4%,发现平均67%的隐性症状,并存在数量有限的问题。