An Intelligent LLM-Based Mental Coping Detection Framework

Ali Alsaifi; Altaf Alfarhan; Shaikhah Alkhadhr; Loulwah Alsumait; Hassah Alheneidi; Ranya Alawadhi

doi:10.3233/SHTI260855

An Intelligent LLM-Based Mental Coping Detection Framework

Ali Alsaifi
, Altaf Alfarhan
, Shaikhah Alkhadhr
, Loulwah Alsumait^*
, Hassah Alheneidi
, Ranya Alawadhi

^*Corresponding author for this work

HBKU College of Science and Engineering

Kuwait University
Abdullah AlSalem University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Social media-based Mental Health (MH) informatics often focuses on disorder detection, overlooking psychological aspects like coping mechanisms and stressors. Furthermore, user-generated texts, although contains MH signals at scale, lack clinical validation. This paper presents a two-stage framework for mental coping detection that combines a coping strategy classifier and a Retrieval-Augmented Generation (RAG) system. The coping classifier identifies coping strategies from user text, while the RAG generates responses grounded in expert-validated knowledge bases and the coping labels. A pilot study demonstrates that the classifier achieves 79.7% accuracy and 0.797 weighted F1 on a held-out dataset. The RAG system produces structured, readable, and expert-verified responses. The classifier-conditioned RAG architecture provides responses grounded in validated psychological knowledge, offering a scalable approach to bridging social media data with evidence-based insights.

Original language	English
Title of host publication	Health Sciences Informatics Leads and Empowers the Digital Health Transformation - 24th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2026
Editors	John Mantas, Arie Hasman, Parisis Gallos, Reinhold Haux, Konstantinos Karitis
Publisher	IOS Press BV
Pages	318-322
Number of pages	5
ISBN (Electronic)	9781643686684
DOIs	https://doi.org/10.3233/SHTI260855
Publication status	Published - 29 Jun 2026
Event	24th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2026 - Athens, Greece Duration: 3 Jul 2026 → 5 Jul 2026

Publication series

Name	Studies in Health Technology and Informatics
Volume	338
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	24th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2026
Country/Territory	Greece
City	Athens
Period	3/07/26 → 5/07/26

Keywords

Health Informatics
Health Psychology
LLM
Mental coping
RAG

Access to Document

10.3233/SHTI260855

Cite this

Alsaifi, A., Alfarhan, A., Alkhadhr, S., Alsumait, L., Alheneidi, H., & Alawadhi, R. (2026). An Intelligent LLM-Based Mental Coping Detection Framework. In J. Mantas, A. Hasman, P. Gallos, R. Haux, & K. Karitis (Eds.), Health Sciences Informatics Leads and Empowers the Digital Health Transformation - 24th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2026 (pp. 318-322). (Studies in Health Technology and Informatics; Vol. 338). IOS Press BV. https://doi.org/10.3233/SHTI260855

Alsaifi, Ali ; Alfarhan, Altaf ; Alkhadhr, Shaikhah et al. / An Intelligent LLM-Based Mental Coping Detection Framework. Health Sciences Informatics Leads and Empowers the Digital Health Transformation - 24th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2026. editor / John Mantas ; Arie Hasman ; Parisis Gallos ; Reinhold Haux ; Konstantinos Karitis. IOS Press BV, 2026. pp. 318-322 (Studies in Health Technology and Informatics).

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title = "An Intelligent LLM-Based Mental Coping Detection Framework",

abstract = "Social media-based Mental Health (MH) informatics often focuses on disorder detection, overlooking psychological aspects like coping mechanisms and stressors. Furthermore, user-generated texts, although contains MH signals at scale, lack clinical validation. This paper presents a two-stage framework for mental coping detection that combines a coping strategy classifier and a Retrieval-Augmented Generation (RAG) system. The coping classifier identifies coping strategies from user text, while the RAG generates responses grounded in expert-validated knowledge bases and the coping labels. A pilot study demonstrates that the classifier achieves 79.7\% accuracy and 0.797 weighted F1 on a held-out dataset. The RAG system produces structured, readable, and expert-verified responses. The classifier-conditioned RAG architecture provides responses grounded in validated psychological knowledge, offering a scalable approach to bridging social media data with evidence-based insights.",

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author = "Ali Alsaifi and Altaf Alfarhan and Shaikhah Alkhadhr and Loulwah Alsumait and Hassah Alheneidi and Ranya Alawadhi",

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year = "2026",

month = jun,

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doi = "10.3233/SHTI260855",

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Alsaifi, A, Alfarhan, A, Alkhadhr, S, Alsumait, L, Alheneidi, H & Alawadhi, R 2026, An Intelligent LLM-Based Mental Coping Detection Framework. in J Mantas, A Hasman, P Gallos, R Haux & K Karitis (eds), Health Sciences Informatics Leads and Empowers the Digital Health Transformation - 24th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2026. Studies in Health Technology and Informatics, vol. 338, IOS Press BV, pp. 318-322, 24th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2026, Athens, Greece, 3/07/26. https://doi.org/10.3233/SHTI260855

An Intelligent LLM-Based Mental Coping Detection Framework. / Alsaifi, Ali; Alfarhan, Altaf; Alkhadhr, Shaikhah et al.
Health Sciences Informatics Leads and Empowers the Digital Health Transformation - 24th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2026. ed. / John Mantas; Arie Hasman; Parisis Gallos; Reinhold Haux; Konstantinos Karitis. IOS Press BV, 2026. p. 318-322 (Studies in Health Technology and Informatics; Vol. 338).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Intelligent LLM-Based Mental Coping Detection Framework

AU - Alsaifi, Ali

AU - Alfarhan, Altaf

AU - Alkhadhr, Shaikhah

AU - Alsumait, Loulwah

AU - Alheneidi, Hassah

AU - Alawadhi, Ranya

PY - 2026/6/29

Y1 - 2026/6/29

N2 - Social media-based Mental Health (MH) informatics often focuses on disorder detection, overlooking psychological aspects like coping mechanisms and stressors. Furthermore, user-generated texts, although contains MH signals at scale, lack clinical validation. This paper presents a two-stage framework for mental coping detection that combines a coping strategy classifier and a Retrieval-Augmented Generation (RAG) system. The coping classifier identifies coping strategies from user text, while the RAG generates responses grounded in expert-validated knowledge bases and the coping labels. A pilot study demonstrates that the classifier achieves 79.7% accuracy and 0.797 weighted F1 on a held-out dataset. The RAG system produces structured, readable, and expert-verified responses. The classifier-conditioned RAG architecture provides responses grounded in validated psychological knowledge, offering a scalable approach to bridging social media data with evidence-based insights.

AB - Social media-based Mental Health (MH) informatics often focuses on disorder detection, overlooking psychological aspects like coping mechanisms and stressors. Furthermore, user-generated texts, although contains MH signals at scale, lack clinical validation. This paper presents a two-stage framework for mental coping detection that combines a coping strategy classifier and a Retrieval-Augmented Generation (RAG) system. The coping classifier identifies coping strategies from user text, while the RAG generates responses grounded in expert-validated knowledge bases and the coping labels. A pilot study demonstrates that the classifier achieves 79.7% accuracy and 0.797 weighted F1 on a held-out dataset. The RAG system produces structured, readable, and expert-verified responses. The classifier-conditioned RAG architecture provides responses grounded in validated psychological knowledge, offering a scalable approach to bridging social media data with evidence-based insights.

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KW - Health Psychology

KW - LLM

KW - Mental coping

KW - RAG

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M3 - Conference contribution

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AN - SCOPUS:105044971733

T3 - Studies in Health Technology and Informatics

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EP - 322

BT - Health Sciences Informatics Leads and Empowers the Digital Health Transformation - 24th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2026

A2 - Mantas, John

A2 - Hasman, Arie

A2 - Gallos, Parisis

A2 - Haux, Reinhold

A2 - Karitis, Konstantinos

PB - IOS Press BV

T2 - 24th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2026

Y2 - 3 July 2026 through 5 July 2026

ER -

Alsaifi A, Alfarhan A, Alkhadhr S, Alsumait L, Alheneidi H, Alawadhi R. An Intelligent LLM-Based Mental Coping Detection Framework. In Mantas J, Hasman A, Gallos P, Haux R, Karitis K, editors, Health Sciences Informatics Leads and Empowers the Digital Health Transformation - 24th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2026. IOS Press BV. 2026. p. 318-322. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI260855

An Intelligent LLM-Based Mental Coping Detection Framework

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