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Detecting and Explaining Postpartum Depression in Real-Time with Generative Artificial Intelligence.

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Název: Detecting and Explaining Postpartum Depression in Real-Time with Generative Artificial Intelligence.
Autoři: García-Méndez, Silvia, de Arriba-Pérez, Francisco
Zdroj: Applied Artificial Intelligence; Dec2025, Vol. 39 Issue 1, p1-27, 27p
Témata: POSTPARTUM depression, NATURAL language processing, LANGUAGE models, DETECTION algorithms, MACHINE learning, GENERATIVE artificial intelligence, MENTAL health, REAL-time computing
Abstrakt: 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) toward an affordable, real-time, and noninvasive 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. [ABSTRACT FROM AUTHOR]
Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Applied Artificial Intelligence; Dec2025, Vol. 39 Issue 1, p1-27, 27p
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  Data: <searchLink fieldCode="DE" term="%22POSTPARTUM+depression%22">POSTPARTUM depression</searchLink><br /><searchLink fieldCode="DE" term="%22NATURAL+language+processing%22">NATURAL language processing</searchLink><br /><searchLink fieldCode="DE" term="%22LANGUAGE+models%22">LANGUAGE models</searchLink><br /><searchLink fieldCode="DE" term="%22DETECTION+algorithms%22">DETECTION algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22GENERATIVE+artificial+intelligence%22">GENERATIVE artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22MENTAL+health%22">MENTAL health</searchLink><br /><searchLink fieldCode="DE" term="%22REAL-time+computing%22">REAL-time computing</searchLink>
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  Data: 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) toward an affordable, real-time, and noninvasive 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. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Dec2025
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