Self-Administered Interventions Based on Natural Language Processing Models for Reducing Depressive and Anxiety Symptoms: Systematic Review and Meta-Analysis.

Journal: JMIR mental health
PMID: 39167795

Abstract

BACKGROUND: The introduction of natural language processing (NLP) technologies has significantly enhanced the potential of self-administered interventions for treating anxiety and depression by improving human-computer interactions. Although these advances, particularly in complex models such as generative artificial intelligence (AI), are highly promising, robust evidence validating the effectiveness of the interventions remains sparse.

Authors

  • David Villarreal-Zegarra
    Universidad Continental, Lima, Peru.
  • C Mahony Reategui-Rivera
    Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, Utah.
  • Jackeline García-Serna
    Instituto Peruano de Orientación Psicológica, Lima, Peru.
  • Gleni Quispe-Callo
    Instituto Peruano de Orientación Psicológica, Lima, Peru.
  • Gabriel Lázaro-Cruz
    Instituto Peruano de Orientación Psicológica, Lima, Peru.
  • Gianfranco Centeno-Terrazas
    Instituto Peruano de Orientación Psicológica, Lima, Peru.
  • Ricardo Galvez-Arevalo
    Instituto Nacional de Salud del Niño San Borja, Lima, Peru.
  • Stefan Escobar-Agreda
    Telehealth Unit, Universidad Nacional Mayor de San Marcos, Lima, Peru.
  • Alejandro Dominguez-Rodriguez
    Psychology, Health, and Technology, University of Twente, Enschede, The Netherlands a.dominguezrodriguez@utwente.nl.
  • Joseph Finkelstein
    Department of Biomedical Informatics, School of Medicine, University of Utah, USA.

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