The symptom checking systems inquire users for their symptoms and perform a rapid and affordable medical assessment of their condition. The basic symptom checking systems based on Bayesian methods, decision trees, or information gain methods are easy to train and do not require significant computational resources. However, their drawbacks are low relevance of proposed symptoms and insufficient quality of diagnostics. The best results on these tasks are achieved by reinforcement learning models. Their weaknesses are the difficulty of developing and training such systems and limited applicability to cases with large and sparse decision spaces. We propose a new approach based on the supervised learning of neural models with logic regularization that combines the advantages of the different methods. Our experiments on real and synthetic data show that the proposed approach outperforms the best existing methods in the accuracy of diagnosis when the number of diagnoses and symptoms is large.

翻译：症状检查系统调查使用者的症状并对他们的病情进行快速和负担得起的医疗评估。基于贝耶斯方法、决策树或信息获取方法的基本症状检查系统很容易培训,不需要大量计算资源。但是,它们的缺点是,拟议的症状的相关性低,诊断质量不足。这些任务的最佳结果是通过强化学习模式实现。这些弱点是难以开发和培训这种系统,而且对决定空间大而稀少的案件的适用性有限。我们建议采用一种新的方法,其基础是监督地学习神经模型,并结合不同方法的优势进行逻辑规范。我们对实际和合成数据的实验表明,在诊断和症状数量大的情况下,拟议的方法在诊断准确性方面超过了现有的最佳方法。