Detecting and explaining postpartum depression in real-time with generative artificial intelligence
This addresses the critical need for rapid detection and intervention of postpartum depression in mothers, though it appears to be an incremental application of existing AI methods to a specific domain.
The paper tackles postpartum depression detection by developing an intelligent screening system that combines NLP, ML, and LLMs for real-time, non-invasive speech analysis, achieving 90% accuracy across all evaluation metrics and outperforming existing solutions.
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.