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Area of Science:

  • Computational linguistics
  • Psychiatry
  • Artificial intelligence

Background:

  • Early depression detection is crucial for effective intervention.
  • Natural language processing (NLP) and machine learning (ML) show potential for automated depression detection from text.
  • Existing evidence on the performance of NLP and ML for depression detection is limited.

Purpose of the Study:

  • To systematically review and meta-analyze studies on NLP and ML for depression detection from spoken or written language.
  • To quantify the performance of automated depression detection methods.
  • To identify factors influencing the performance of these methods.

Main Methods:

  • Systematic review and meta-analysis of studies identified through six electronic databases and additional sources.
  • Quantitative synthesis of data from 123 eligible articles, with one representative result per dataset.
  • Pooled analysis of accuracy, precision, recall, AUC, and balanced accuracy from 43 studies involving 40,983 text samples.

Main Results:

  • Pooled accuracy was 0.80, with pooled precision of 0.78 and recall of 0.76.
  • Subgroup analyses revealed significant variations based on language, text source, feature type, and classifier.
  • Text source was the only significant predictor in meta-regressions, explaining 13.6% of the variance.

Conclusions:

  • Automated depression detection from text demonstrates promising performance but exhibits substantial heterogeneity.
  • Findings highlight both the potential and limitations of text-based depression detection.
  • Methodological standardization and validation are essential before widespread clinical adoption.