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Attention-Enabled Ensemble Deep Learning Models and Their Validation for Depression Detection: A Domain Adoption

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Attention-enabled ensemble deep learning (aeEDL) models significantly improve depression detection accuracy across different domains. These advanced models outperform traditional methods, offering a more generalized and effective approach for identifying depression symptoms.

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Area of Science:

  • Computational linguistics and natural language processing.
  • Artificial intelligence and machine learning applications in healthcare.
  • Psychiatric informatics and computational psychiatry.

Background:

  • Depression is a growing global health concern with increased suicide risks.
  • Accurate depression detection using text analysis in cross-domain settings remains a significant challenge.
  • Existing solo deep learning (SDL) and ensemble deep learning (EDL) models lack sufficient robustness.

Purpose of the Study:

  • To investigate the efficacy of attention-enabled ensemble deep learning (aeEDL) architectures for depression detection.
  • To compare the performance of aeEDL against attention-not-enabled SDL (aneSDL), attention-enabled SDL (aeSDL), and attention-not-enabled EDL (aneEDL) models.
  • To validate the generalizability and effectiveness of aeEDL in cross-domain sentiment analysis for depression detection.

Main Methods:

  • Development of EDL-based architectures incorporating attention blocks for both SDL and EDL models.
  • Training and evaluation of eleven SDL and five EDL models on four domain-specific datasets.
  • Scientific validation using 'seen' and 'unseen' paradigms (SUP) and benchmarking against the SemEval (2016) dataset.

Main Results:

  • EDL models showed a mean accuracy increase of 4.49% over corresponding SDL components.
  • Attention mechanisms improved mean accuracy (AUC) by 2.58% (1.73%) for aeSDL over aneSDL and 2.76% (2.80%) for aeEDL over aneEDL.
  • aeEDL models consistently outperformed SDL counterparts, with the best aeEDL (ALBERT+BERT-BiLSTM) surpassing the best aeSDL (BERT-BiLSTM) by 3.86% on the SemEval dataset.

Conclusions:

  • Attention-enabled ensemble deep learning (aeEDL) architectures are superior for depression detection in cross-domain settings.
  • The proposed aeEDL method demonstrates high effectiveness and generalizability, meeting stringent validation criteria.
  • This research validates the hypothesis that incorporating attention mechanisms into EDL models significantly enhances depression symptom detection.