README.md

metadata

title: Early Depression Detection MCP Agent
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 6.0.1
app_file: app.py
pinned: true
tags:
  - mcp-in-action-track-consumer
  - depression-detection
  - mental-health
  - longformer
  - mcp
  - agents
license: mit
short_description: MCP-enabled depression detection agent

🧠 Early Depression Detection MCP Agent

Hackathon: MCP 1st Birthday - Track 2: MCP in Action (Consumer)
Author: Hassan Hassanzadeh Aliabadi | LinkedIn

📹 Demo Video

[INSERT YOUTUBE/LOOM LINK HERE]

📱 Social Media Post

[INSERT LINKEDIN/X POST LINK HERE]

🎯 Project Description

This MCP-enabled agent detects depression risk from social media text using Mental-Longformer, achieving F1-score of 0.7668 on eRisk 2025 test data.

Key Features:

• 🔍 4,096-token context window (8x BERT's capacity)
• 📊 Trained on eRisk 2017-2022 datasets
• 🤖 Data augmentation with Gemini 2.5 Flash Lite
• ⚡ Real-time linguistic pattern analysis
• 🔌 MCP-enabled for agent integration

Model: avtak/erisk-longformer-depression-v1

🧪 How It Works

The agent analyzes long-form text for linguistic markers including:

• Anhedonia (loss of interest)
• Self-focused negative language
• Social withdrawal indicators
• Hopelessness themes
• Disrupted sleep/energy patterns

🏆 Research Background

Built on Master's thesis research at University of Malaya, this model addresses critical challenges in early depression detection:

• Handles imbalanced datasets through LLM-powered augmentation
• Captures long-context dependencies (4096 vs 512 tokens)
• Rigorous 5-fold cross-validation (mean F1: 0.862, std: 0.009)
• Validated on held-out eRisk 2025 test set

👥 Team

• Hassan Hassanzadeh Aliabadi (@avtak)

⚠️ Ethical Considerations

This is a research tool, not a medical diagnostic instrument. Always consult qualified healthcare professionals for mental health concerns.

Crisis Resources:

• 🆘 Crisis Text Line: Text HOME to 741741 (US)
• 🌍 International: befrienders.org