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Natural language processing applied to mental illness detection: a narrative review.

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Natural Language Processing (NLP) is advancing mental illness detection. Recent research shows deep learning methods outperform traditional approaches, aiding proactive mental healthcare and early diagnosis.

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Area of Science:

  • Computational linguistics
  • Psychiatry
  • Digital health

Background:

  • Mental illness is a prevalent global health issue with significant societal impact.
  • Complex multifactorial nature of mental illness necessitates advanced analytical tools.
  • Textual data from diverse sources (social media, clinical notes) contain valuable insights.

Purpose of the Study:

  • To conduct a narrative review of Natural Language Processing (NLP) for mental illness detection over the past decade.
  • To identify current methods, research trends, challenges, and future directions in the field.
  • To synthesize findings from 399 studies to understand the evolution of NLP in mental healthcare.

Main Methods:

  • Systematic literature search across multiple databases.
  • Inclusion criteria focused on NLP applications for mental illness detection.
  • Narrative synthesis and trend analysis of selected studies.

Main Results:

  • A significant upward trend in NLP research for mental illness detection was observed.
  • Deep learning methods are increasingly prominent and demonstrate superior performance compared to traditional machine learning.
  • The review identified key challenges and emerging opportunities in the field.

Conclusions:

  • NLP, particularly deep learning, shows substantial promise for enhancing proactive mental healthcare and early diagnosis.
  • Further research is recommended to develop novel detection methods and interpretable deep learning models.
  • Continued advancements in NLP can significantly contribute to understanding and managing mental health conditions.