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

• URL: http://arxiv.org/abs/2508.10025v1
• Date: Fri, 08 Aug 2025 07:57:05 GMT
• Title: Detecting and explaining postpartum depression in real-time with generative artificial intelligence
• Authors: Silvia García-Méndez, Francisco de Arriba-Pérez,
• Abstract summary: This work contributes to an intelligent PPD screening system that combines Natural Language Processing, Machine Learning, and Large Language Models.<n>The results obtained are 90 % on ppd detection for all evaluation metrics, outperforming the competing solutions in the literature.
• Score: 5.635300481123079
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• Systematic Evaluation of Machine-Generated Reasoning and PHQ-9 Labeling for Depression Detection Using Large Language Models [5.426680341952808]
  Large language models (LLMs) for early mental health detection, such as depression, are often optimized with machine-generated data.<n>Here, we provide a systematic evaluation of the reasoning over machine-generated detection and interpretation.<n>We then use the models' reasoning abilities to explore mitigation strategies for enhanced performance.
  arXiv  Detail & Related papers  (2025-05-21T16:30:50Z)
• Generating Medically-Informed Explanations for Depression Detection using LLMs [1.325953054381901]
  Early detection of depression from social media data offers a valuable opportunity for timely intervention.<n>We propose LLM-MTD (Large Language Model for Multi-Task Depression Detection), a novel approach that combines the power of large language models with the crucial aspect of explainability.
  arXiv  Detail & Related papers  (2025-03-18T19:23:22Z)
• Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases [48.87360916431396]
  We introduce MedR-Bench, a benchmarking dataset of 1,453 structured patient cases, annotated with reasoning references.<n>We propose a framework encompassing three critical examination recommendation, diagnostic decision-making, and treatment planning, simulating the entire patient care journey.<n>Using this benchmark, we evaluate five state-of-the-art reasoning LLMs, including DeepSeek-R1, OpenAI-o3-mini, and Gemini-2.0-Flash Thinking, etc.
  arXiv  Detail & Related papers  (2025-03-06T18:35:39Z)
• LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment [75.44934940580112]
  This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment.<n>We employ a zero-shot prompting strategy with carefully designed cues to guide the model in interpreting and scoring transcribed clinical interviews.<n>Our approach, tested on 236 real-world interviews, demonstrates strong correlations with clinician assessments.
  arXiv  Detail & Related papers  (2025-01-07T08:49:04Z)
• Leveraging Large Language Models through Natural Language Processing to provide interpretable Machine Learning predictions of mental deterioration in real time [5.635300481123079]
  Based on official estimates, 50 million people worldwide are affected by dementia, and this number increases by 10 million new patients every year. To this end, Artificial Intelligence and computational linguistics can be exploited for natural language analysis, personalized assessment, monitoring, and treatment. We contribute with an affordable, flexible, non-invasive, personalized diagnostic system to this work.
  arXiv  Detail & Related papers  (2024-09-05T09:27:05Z)
• I-SIRch: AI-Powered Concept Annotation Tool For Equitable Extraction And Analysis Of Safety Insights From Maternity Investigations [0.8609957371651683]
  Most current tools for analysing healthcare data focus only on biomedical concepts, overlooking the importance of human factors. We developed I-SIRch, using artificial intelligence to automatically identify and label human factors concepts. I-SIRch was trained using real data and tested on both real and simulated data to evaluate its performance in identifying human factors concepts.
  arXiv  Detail & Related papers  (2024-06-08T16:05:31Z)
• Assessing ML Classification Algorithms and NLP Techniques for Depression Detection: An Experimental Case Study [0.6524460254566905]
  Depression has affected millions of people worldwide and has become one of the most common mental disorders. Recent research has evidenced that machine learning (ML) and Natural Language Processing (NLP) tools and techniques have significantly been used to diagnose depression. However, there are still several challenges in the assessment of depression detection approaches in which other conditions such as post-traumatic stress disorder (PTSD) are present.
  arXiv  Detail & Related papers  (2024-04-03T19:45:40Z)
• Depression Detection on Social Media with Large Language Models [23.075317886505193]
  Depression detection aims to determine whether an individual suffers from depression by analyzing their history of posts on social media. We propose a novel depression detection system called DORIS, combining medical knowledge and the recent advances in large language models.
  arXiv  Detail & Related papers  (2024-03-16T01:01:16Z)
• Clairvoyance: A Pipeline Toolkit for Medical Time Series [95.22483029602921]
  Time-series learning is the bread and butter of data-driven *clinical decision support* Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a software toolkit. Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
  arXiv  Detail & Related papers  (2023-10-28T12:08:03Z)
• Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
  Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning. They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health. Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
  arXiv  Detail & Related papers  (2023-05-30T22:05:11Z)
• Predicting Adverse Neonatal Outcomes for Preterm Neonates with Multi-Task Learning [51.487856868285995]
  We first analyze the correlations between three adverse neonatal outcomes and then formulate the diagnosis of multiple neonatal outcomes as a multi-task learning (MTL) problem. In particular, the MTL framework contains shared hidden layers and multiple task-specific branches.
  arXiv  Detail & Related papers  (2023-03-28T00:44:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.