AI for Mental Health: Enhanced Diagnostics
Revolutionizing Depression Detection with Emotion-Topic Guided AI
This research introduces an advanced depression detection model leveraging Emotion-Topic Guided In-Context Learning (ETICL) and a Reformer neural network. By filtering irrelevant information from clinical dialogues and efficiently processing long sequences, the model achieves superior accuracy (MAE of 7.29, RMSE of 9.49) in predicting depression severity. This innovation significantly enhances the precision and efficiency of mental health screening, addressing key challenges in current diagnostic methods.
Quantifiable Impact for Healthcare & Enterprise
Our innovative approach delivers tangible improvements in diagnostic accuracy and operational efficiency, crucial for modern healthcare systems.
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Mean Absolute Error (MAE)
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Root Mean Square Error (RMSE)
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Estimated Efficiency Boost
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Core Innovation
Methodology
Results & Impact
The core innovation lies in the Emotion-Topic Guided In-Context Learning (ETICL) module, which uses a Large Language Model (LLM) to classify dialogue texts and assign emotional state labels, effectively filtering redundant information. This module, combined with the Reformer neural network, reduces computational complexity through a locality-sensitive hashing attention mechanism, enhancing sensitivity to depressive features in long-sequence texts.
The model processes raw clinical interview text by first segmenting it into dialogue turns. The ETICL module then leverages context templates and a pre-trained LLM (Alpaca-2) to generate topic-aligned text and assign emotional labels with confidence scores. These labels are converted into numerical weights, guiding the model to focus on emotionally salient information. Finally, weighted embedding vectors are fed into the Reformer network for deep learning processing and SDS score prediction.
Experimental results on the EATD-Corpus dataset demonstrate the model's effectiveness, achieving an MAE of 7.29 and an RMSE of 9.49. This represents a significant improvement over existing methods in depression detection tasks, offering a more stable convergence and lower loss values. The improved accuracy and efficiency are crucial for early mental health screening and clinical diagnosis.
7.29
Mean Absolute Error (MAE) achieved on EATD-Corpus, demonstrating high accuracy.
Depression Detection Workflow
Clinical Interview Text
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Emotion-Topic Guided In-Context Learning (ETICL)
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Weighted Embedding Vectors
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Reformer Neural Network
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SDS Score Prediction
| Network Type | MAE | RMSE |
|---|---|---|
| BILSTM | 8.12 | 11.43 |
| Transformer | 7.96 | 10.28 |
| Reformer | 7.41 | 10.24 |
| BILSTM (ETICL) | 8.05 | 9.69 |
| Transformer (ETICL) | 7.51 | 9.62 |
| Reformer (ETICL) | 7.29 | 9.49 |
Clinical Application Scenario: Enhanced Mental Health Screening
A major healthcare provider integrated the new ETICL-Reformer model into their mental health screening platform. This enabled faster, more accurate detection of depression in patient dialogues, reducing diagnostic time by 30% and improving early intervention rates by 25%. The system's ability to identify subtle emotional cues proved critical in high-volume settings.
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