Diagnosis-oriented dialogue system queries the patient's health condition and makes predictions about possible diseases through continuous interaction with the patient. A few studies use reinforcement learning (RL) to learn the optimal policy from the joint action space of symptoms and diseases. However, existing RL (or Non-RL) methods cannot achieve sufficiently good prediction accuracy, still far from its upper limit. To address the problem, we propose a decoupled automatic diagnostic framework DxFormer, which divides the diagnosis process into two steps: symptom inquiry and disease diagnosis, where the transition from symptom inquiry to disease diagnosis is explicitly determined by the stopping criteria. In DxFormer, we treat each symptom as a token, and formalize the symptom inquiry and disease diagnosis to a language generation model and a sequence classification model respectively. We use the inverted version of Transformer, i.e., the decoder-encoder structure, to learn the representation of symptoms by jointly optimizing the reinforce reward and cross entropy loss. Extensive experiments on three public real-world datasets prove that our proposed model can effectively learn doctors' clinical experience and achieve the state-of-the-art results in terms of symptom recall and diagnostic accuracy.
翻译:诊断性对话系统询问患者的健康状况,并通过与患者的持续互动预测可能的疾病。一些研究利用强化学习(RL)从症状和疾病的联合行动空间学习最佳政策。然而,现有的RL(或非RL)方法不能达到足够好的预测准确性,远远没有达到其上限。为解决这一问题,我们提议了一个分解的自动诊断框架DxFormer,将诊断过程分为两个步骤:症状调查和疾病诊断,从症状调查到疾病诊断的过渡由停止标准明确确定。在DxFormer,我们将每一种症状作为象征,并将症状调查和疾病诊断正式化为语言生成模型和序列分类模型。我们使用变形器的倒置版本,即解析器构件结构,通过共同优化强化的奖赏和交叉酶损失来了解症状的表现形式。关于三个公共真实数据集的广泛实验证明,我们拟议的模型可以有效地学习医生临床经验,并在重新获得状态诊断结果和诊断结果中实现状态的准确性。