"Synthetic Data Generation with LLM for Improved Depression Prediction"

LLMs now create realistic patient data, helping doctors spot depression without privacy risks.

The research introduces a two-step LLM pipeline that generates synthetic clinical interview data to enhance depression prediction while preserving privacy and statistical properties.

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https://arxiv.org/abs/2411.17672

🤔 Original Problem:

Limited clinical data for mental health research hampers model development. Privacy concerns and data scarcity in depression diagnosis create barriers for effective automated screening systems.

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🔧 Solution in this Paper:

→ A two-step LLM pipeline processes clinical interviews using chain-of-thought prompting

→ Step 1 converts verbose transcripts into concise synopses with sentiment analysis

→ Step 2 generates synthetic versions with varied depression severity levels

→ Meta Llama 3.2-3B Instruct model processes DAIC-WOZ dataset containing 189 clinical interviews

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💡 Key Insights:

→ Synthetic data maintains statistical properties while protecting privacy

→ Chain-of-thought prompting improves data generation quality

→ Combined real and synthetic data enhances model performance

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📊 Results:

→ RMSE: 4.64 and MAE: 3.66 in depression score prediction

→ 15% improvement over baseline models

→ Better balanced distribution across depression severity levels