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<p>Good mental health is crucial for well-being. Unfortunately, despite the advancements of automatic detection solutions in the mental health field, along with the existence of effective treatments, a large percentage of affected people receive no care for their disorder. Thus, this research proposes an innovative framework integrating counterfactual explanations into a multi-label detection system for anxiety and depression, combining large language models for feature extraction and multi-label machine learning for final prediction. The solution is designed to operate in a streaming mode, addressing the need to process information in real-time. Moreover, sliding window techniques manage the data’s evolution, preserving temporal relevance while ensuring robust, user-centered interpretation capabilities. The latter is reinforced by the generation of counterfactual explanations, which contribute to the interpretability, adaptability, and accountability of the results in a critical context, such as mental health. The results surpass the 90% accuracy, indicating very few misclassifications per label. Ultimately, this solution contributes to the literature with timely and transparent decision-making in mental healthcare.</p>

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Continuous Monitoring of Mental Health through Streaming Machine Learning with Counterfactual Explanations

  • 期刊论文

  • 发表日期：

    2025年12月17日

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  • Francisco de Arriba-Pérez,
  • Silvia García-Méndez

摘要

Good mental health is crucial for well-being. Unfortunately, despite the advancements of automatic detection solutions in the mental health field, along with the existence of effective treatments, a large percentage of affected people receive no care for their disorder. Thus, this research proposes an innovative framework integrating counterfactual explanations into a multi-label detection system for anxiety and depression, combining large language models for feature extraction and multi-label machine learning for final prediction. The solution is designed to operate in a streaming mode, addressing the need to process information in real-time. Moreover, sliding window techniques manage the data’s evolution, preserving temporal relevance while ensuring robust, user-centered interpretation capabilities. The latter is reinforced by the generation of counterfactual explanations, which contribute to the interpretability, adaptability, and accountability of the results in a critical context, such as mental health. The results surpass the 90% accuracy, indicating very few misclassifications per label. Ultimately, this solution contributes to the literature with timely and transparent decision-making in mental healthcare.