In response to the global challenge of mental health problems, we proposes a Logical Neural Network (LNN) based Neuro-Symbolic AI method for the diagnosis of mental disorders. Due to the lack of effective therapy coverage for mental disorders, there is a need for an AI solution that can assist therapists with the diagnosis. However, current Neural Network models lack explainability and may not be trusted by therapists. The LNN is a Recurrent Neural Network architecture that combines the learning capabilities of neural networks with the reasoning capabilities of classical logic-based AI. The proposed system uses input predicates from clinical interviews to output a mental disorder class, and different predicate pruning techniques are used to achieve scalability and higher scores. In addition, we provide an insight extraction method to aid therapists with their diagnosis. The proposed system addresses the lack of explainability of current Neural Network models and provides a more trustworthy solution for mental disorder diagnosis.

翻译：应对全球精神健康问题的挑战，我们提出一种基于逻辑神经网络（LNN）的神经符号AI方法，用于精神障碍的诊断。由于精神障碍的有效治疗覆盖率不足，亟需一种能够辅助治疗师进行诊断的AI解决方案。然而，当前的神经网络模型缺乏可解释性，可能难以获得治疗师的信任。LNN是一种递归神经网络架构，它结合了神经网络的学习能力与经典逻辑推理能力。所提出的系统通过输入临床访谈中的谓词，输出精神障碍类别，并采用多种谓词剪枝技术以实现可扩展性和更高评分。此外，我们提供了一种见解提取方法，以辅助治疗师进行诊断。该系统解决了当前神经网络模型可解释性不足的问题，为精神障碍诊断提供了更可信的解决方案。