Detecting and explaining postpartum depression in real-time with generative artificial intelligence

TL;DR

This paper presents an AI-based system that detects postpartum depression in real-time through speech analysis, combining NLP, ML, and LLMs to provide interpretable and rapid screening to aid timely intervention.

Contribution

It introduces an innovative, interpretable AI system that integrates LLMs and ML for real-time postpartum depression detection using speech analysis.

Findings

Achieved 90% detection accuracy across evaluation metrics.

Outperformed existing solutions in postpartum depression detection.

Provided interpretable predictions with feature importance and natural language explanations.

Abstract

Among the many challenges mothers undergo after childbirth, postpartum depression (PPD) is a severe condition that significantly impacts their mental and physical well-being. Consequently, the rapid detection of ppd and their associated risk factors is critical for in-time assessment and intervention through specialized prevention procedures. Accordingly, this work addresses the need to help practitioners make decisions with the latest technological advancements to enable real-time screening and treatment recommendations. Mainly, our work contributes to an intelligent PPD screening system that combines Natural Language Processing, Machine Learning (ML), and Large Language Models (LLMs) towards an affordable, real-time, and non-invasive free speech analysis. Moreover, it addresses the black box problem since the predictions are described to the end users thanks to the combination of LLMs with interpretable ml models (i.e., tree-based algorithms) using feature importance and natural language. The results obtained are 90 % on ppd detection for all evaluation metrics, outperforming the competing solutions in the literature. Ultimately, our solution contributes to the rapid detection of PPD and their associated risk factors, critical for in-time and proper assessment and intervention.