Detecting anxiety and depression in dialogues: a multi-label and explainable approach

TL;DR

This paper introduces a novel multi-label system using Large Language Models and machine learning for detecting anxiety and depression in dialogues, with explainability features, achieving 90% accuracy on real data.

Contribution

It presents a new multi-label classification system leveraging LLMs and ML, with explainability, for early mental health detection from dialogue data.

Findings

Achieved 90% accuracy on real dataset

Improved upon previous literature results

Provides explainability through graphical dashboards

Abstract

Anxiety and depression are the most common mental health issues worldwide, affecting a non-negligible part of the population. Accordingly, stakeholders, including governments' health systems, are developing new strategies to promote early detection and prevention from a holistic perspective (i.e., addressing several disorders simultaneously). In this work, an entirely novel system for the multi-label classification of anxiety and depression is proposed. The input data consists of dialogues from user interactions with an assistant chatbot. Another relevant contribution lies in using Large Language Models (LLMs) for feature extraction, provided the complexity and variability of language. The combination of LLMs, given their high capability for language understanding, and Machine Learning (ML) models, provided their contextual knowledge about the classification problem thanks to the labeled data, constitute a promising approach towards mental health assessment. To promote the solution's trustworthiness, reliability, and accountability, explainability descriptions of the model's decision are provided in a graphical dashboard. Experimental results on a real dataset attain 90 % accuracy, improving those in the prior literature. The ultimate objective is to contribute in an accessible and scalable way before formal treatment occurs in the healthcare systems.