Explainable AI for time series prediction in economic mental health analysis
IntroductionThe integration of Explainable Artificial Intelligence (XAI) into time series prediction plays a pivotal role in advancing economic mental health analysis, ensuring both transparency and interpretability in predictive models. Traditional deep learning approaches, while highly accurate, o...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1591793/full |
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| Summary: | IntroductionThe integration of Explainable Artificial Intelligence (XAI) into time series prediction plays a pivotal role in advancing economic mental health analysis, ensuring both transparency and interpretability in predictive models. Traditional deep learning approaches, while highly accurate, often operate as black boxes, making them less suitable for high-stakes domains such as mental health forecasting, where explainability is critical for trust and decision-making. Existing post-hoc explainability methods provide only partial insights, limiting their practical application in sensitive domains like mental health analytics.MethodsTo address these challenges, we propose a novel framework that integrates explainability directly within the time series prediction process, combining both intrinsic and post-hoc interpretability techniques. Our approach systematically incorporates feature attribution, causal reasoning, and human-centric explanation generation using an interpretable model architecture.ResultsExperimental results demonstrate that our method maintains competitive accuracy while significantly improving interpretability. The proposed framework supports more informed decision-making for policymakers and mental health professionals.DiscussionThis framework ensures that AI-driven mental health screening tools remain not only highly accurate but also trustworthy, interpretable, and aligned with domain-specific knowledge, ultimately bridging the gap between predictive performance and human understanding. |
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| ISSN: | 2296-858X |