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Background <p>Escalating societal and occupational pressures have exacerbated the global mental health crisis. The symptomatic overlap between depression and anxiety presents a formidable diagnostic hurdle for differential diagnosis in mental health. developing AI-driven diagnostic models to differentiate these states and streamlining complex clinical instruments is of substantial clinical and scientific value.</p> Methods <p>Utilizing clinical questionnaires and physician-confirmed diagnoses, distinct datasets were established. This study evaluates four dimensions: model architecture, data strategy optimization, interpretability, and clinical instrument streamlining. Four resampling strategies and tabular generative models were integrated to address data imbalance. Through stratified cross-validation, 29 models—spanning traditional Machine Learning (ML), sequential Deep Learning (DL), and advanced tabular DL—were compared. Optimization strategies focusing on input representation and generalization were also evaluated.</p> Results <p>Data strategies significantly enhanced standard deep learning architectures. Specifically, Mambular-TTT combined with Borderline-SMOTE and TVAE achieved a 7.3% relative AUC improvement over the best-performing traditional machine learning baseline. Hierarchical SHAP identified childhood trauma and neuroticism as shared correlated predictive features while revealing subtype-specific feature association patterns. Stepwise reduction revealed that a 7-instrument subset optimally balances diagnostic performance and clinical efficiency; the Area Under the Receiver Operating Characteristic Curve (AUC) for the streamlined Dataset 2 improved to 0.70, outperforming the full-scale model.</p> Conclusions <p>This study demonstrates that reliable AI-assisted diagnosis requires the integrated optimization of model architecture and data strategy. This approach, combined with an interpretability-based scale streamlining strategy, provides a practical and evidence-based implementation path for transforming machine learning models into efficient, deployable clinical diagnostic tools.</p>

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Architectures and strategies for the differential diagnosis of adolescent depression and anxiety: bridging tabular deep learning, interpretability, and scale streamlining

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月1日

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  • Ruibing Sun,
  • Yutian Wu,
  • Zhongze Luo,
  • Qizhi Zheng,
  • Yanhong Bai,
  • Xudong Pan,
  • Xiyu Ding,
  • Liping Liu,
  • Guanghui Dong,
  • Xuemei Guan

摘要

Background

Escalating societal and occupational pressures have exacerbated the global mental health crisis. The symptomatic overlap between depression and anxiety presents a formidable diagnostic hurdle for differential diagnosis in mental health. developing AI-driven diagnostic models to differentiate these states and streamlining complex clinical instruments is of substantial clinical and scientific value.

Methods

Utilizing clinical questionnaires and physician-confirmed diagnoses, distinct datasets were established. This study evaluates four dimensions: model architecture, data strategy optimization, interpretability, and clinical instrument streamlining. Four resampling strategies and tabular generative models were integrated to address data imbalance. Through stratified cross-validation, 29 models—spanning traditional Machine Learning (ML), sequential Deep Learning (DL), and advanced tabular DL—were compared. Optimization strategies focusing on input representation and generalization were also evaluated.

Results

Data strategies significantly enhanced standard deep learning architectures. Specifically, Mambular-TTT combined with Borderline-SMOTE and TVAE achieved a 7.3% relative AUC improvement over the best-performing traditional machine learning baseline. Hierarchical SHAP identified childhood trauma and neuroticism as shared correlated predictive features while revealing subtype-specific feature association patterns. Stepwise reduction revealed that a 7-instrument subset optimally balances diagnostic performance and clinical efficiency; the Area Under the Receiver Operating Characteristic Curve (AUC) for the streamlined Dataset 2 improved to 0.70, outperforming the full-scale model.

Conclusions

This study demonstrates that reliable AI-assisted diagnosis requires the integrated optimization of model architecture and data strategy. This approach, combined with an interpretability-based scale streamlining strategy, provides a practical and evidence-based implementation path for transforming machine learning models into efficient, deployable clinical diagnostic tools.