Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosis

TL;DR

This paper introduces a Logical Neural Network-based AI system that combines neural learning with logical reasoning to improve explainability and assist therapists in diagnosing mental disorders.

Contribution

It presents a novel Neuro-Symbolic AI approach using LNNs for mental disorder diagnosis, enhancing explainability and trustworthiness over traditional neural networks.

Findings

Achieved higher diagnostic accuracy with predicate pruning techniques.

Provided an insight extraction method for better interpretability.

Addressed explainability issues in neural network models for mental health.

Abstract

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.