ocd-detector
OCD Detector
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An AI-powered system for detecting and analyzing OCD patterns in digital communications
Overview
OCD Detector is a cutting-edge artificial intelligence system designed to identify and analyze Obsessive-Compulsive Disorder (OCD) patterns across multiple communication channels. By leveraging advanced machine learning technologies, our system provides valuable insights for mental health professionals and researchers.
The National Institute of Mental Health (NIMH) research indicates that individuals with OCD exhibit distinct patterns in their communication and behavioral manifestations[²], including:
- Repetitive thoughts and behaviors
- Specific language patterns and word choices
- Time-consuming rituals that interfere with daily activities
- Persistent intrusive thoughts
Our system aligns with the NIH’s dimensional approach to OCD assessment[³], analyzing these patterns across digital communications to support clinical evaluation and research.
Key Technologies
- 🧠 Advanced Natural Language Processing (NLP)
- 👁️ Optical Character Recognition (OCR)
- 🎤 Voice Recognition and Analysis
- 🤖 Integration with OpenAI’s GPT models
- 📱 CoreML Support for Apple Devices
Methodology
Our OCD detection system employs sophisticated algorithms and statistical methods to analyze behavioral patterns. See Research for more details.
The core methodology includes:
Pattern Recognition
We utilize a modified attention mechanism that weighs behavioral markers ($b_i$) against temporal frequency ($f_t$):
\[A(b, f) = \sum_{i=1}^{n} \frac{b_i \cdot f_t}{\sqrt{d_k}}\]where $d_k$ represents the dimensionality of the behavioral space.
Severity Scoring
The severity score ($S$) is calculated using a weighted combination of detected patterns:
\[S = \alpha \sum_{i=1}^{n} w_i p_i + \beta \log(\frac{f_{obs}}{f_{exp}})\]where:
- $w_i$ represents pattern weights
- $p_i$ represents pattern intensities
- $f_{obs}$ is observed frequency
- $f_{exp}$ is expected frequency
- $\alpha, \beta$ are tuning parameters
Confidence Estimation
Our confidence metric ($C$) incorporates both model uncertainty and data quality:
\[C = \left(1 - \frac{\sigma_m^2}{\sigma_{max}^2}\right) \cdot \left(\frac{n_{valid}}{n_{total}}\right)\]where $\sigma_m^2$ represents model variance and $n_{valid}$ represents the number of valid data points.
Research Used
This project is based on published scientific research papers, please refer to the Research for more details.
Features
Multi-Modal Analysis
- Text Analysis
- Real-time chat monitoring
- Historical conversation analysis
- Pattern recognition in messaging
- Sentiment analysis
- Image Processing
- Screenshot analysis
- Text extraction from images
- Visual pattern recognition
- Metadata analysis
- Voice Recognition
- Speech-to-text conversion
- Vocal pattern analysis
- Tone and rhythm detection
- Real-time monitoring
AI Integration
- Custom fine-tuned models for OCD detection
- Integration with OpenAI’s ChatGPT
- Expandable model architecture
- Transfer learning capabilities
Deployment Options
- Cloud-based deployment
- On-premise installation
- Mobile deployment via CoreML
- Edge device support
Quick Start
Prerequisites
- Python 3.8+
- Node.js 16+
- Tesseract OCR
- GPU recommended for training
Basic Installation
# Clone the repository
git clone https://github.com/skytells-research/ocd-detector.git
# Install dependencies
cd ocd-detector
pip install -r requirements.txt
# Start the application
python run.py
For detailed installation instructions, see Installation Guide.
Documentation
- Installation Guide
- Training Guide
- API Reference
- Model Architecture
- Contributing Guidelines
- Code of Conduct
Use Cases
Clinical Research
- Pattern identification in patient communications
- Long-term behavior tracking
- Treatment effectiveness monitoring
Mental Health Support
- Early detection of OCD tendencies
- Progress monitoring
- Intervention timing optimization
Personal Monitoring
- Self-awareness tools
- Progress tracking
- Pattern identification
Model Performance
| Analysis Type | Accuracy | Precision | Recall |
|---|---|---|---|
| Text | 89% | 87% | 91% |
| Image | 76% | 79% | 74% |
| Voice | 82% | 84% | 81% |
Contributing
We welcome contributions from the community! Please read our Contributing Guidelines before submitting pull requests.
Development Setup
# Create virtual environment
python -m venv venv
source venv/bin/activate
# Install development dependencies
pip install -r requirements-dev.txt
# Run tests
pytest
Running the Application
Running the Frontend
To start the frontend application on port 8502:
streamlit run frontend/app.py --server.port 8502
Running the Backend
Please refer to the Running the Backend for detailed instructions.
Citation
If you use this project in your research, please cite:
@software{ocd_detector2024,
author = {Skytells Research},
title = {OCD Detector: AI-Powered OCD Pattern Analysis},
year = {2024},
url = {https://github.com/skytells-research/ocd-detector}
}
Support
Contributors
Sponsor The Project
If you find this project useful, consider sponsoring us on GitHub to support ongoing development and maintenance. Sponsor Us
Acknowledgments
- OpenAI for GPT integration support
- TensorFlow team for model architecture insights
- Our amazing contributors and research partners
References
[¹] World Health Organization. (2022). Obsessive-compulsive disorder fact sheet. WHO Mental Health Atlas.
https://www.who.int/news-room/fact-sheets/detail/obsessive-compulsive-disorder[²] National Institute of Mental Health. (2023). Obsessive-Compulsive Disorder: Signs, Symptoms and Treatment.
https://www.nimh.nih.gov/health/topics/obsessive-compulsive-disorder-ocd[³] Mataix-Cols, D., et al. (2016). Dimensional Assessment of Obsessive-Compulsive Disorder: National Institute of Mental Health Proceedings. NIH Public Access.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4332094/
License
This project is licensed under the MIT License - see the LICENSE file for details.
For more info, Please contact Skytells Research on our website