Utterance Classification with Logical Neural Network: Explainable AI for
  Mental Disorder Diagnosis
Yeldar Toleubay Don Joven Agravante Daiki Kimura Baihan Lin Djallel Bouneffouf Michiaki Tatsubori
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.