Project Artifacts¶
Research Deliverables and Reproducible Outputs¶
This section summarizes the primary artifacts generated during the development of the CS316 Mental Health AI Project.
All artifacts are structured to support transparency, reproducibility, and academic evaluation.
Core Research Artifacts¶
- IEEE-Style Research Paper
Location: paper/paper.md
Complete academic documentation including methodology, longitudinal alert framework, experimental results, and ethical analysis.
- Synthetic Longitudinal Dataset
Location: datasets/mental_health_arabic_dataset.xlsx
2,500 Arabic text entries with depression and anxiety severity labels across simulated time progression.
- Trained Model Files
Format: .pkl serialized models
Independent SVM classifiers for depression and anxiety severity prediction.
Methodology and Experimental Documentation¶
- Methodology
Location: methodology/
Covers data generation, embedding strategy, model architecture, and evaluation pipeline.
- Results and Analysis
Location: results/
Includes quantitative metrics, ROC analysis, embedding visualization, and error interpretation.
- Limitations
Located within the results documentation to outline methodological and practical constraints.
Deployment and Interface Artifacts¶
- Prototype Application
Location: deployment/app.md
Demonstrates severity prediction and longitudinal alert logic through an interactive interface.
- API Interface Documentation
Location: deployment/api.md
Documents programmatic access layer if implemented.
- Installation Instructions
Location: deployment/install.md
Environment setup and documentation preview guidance.
Presentation Materials¶
- Slides
Location: paper/slides.md
Academic presentation summarizing research objectives, methodology, and results.
- Supporting Artifacts
Location: paper/artifacts.md
Supplementary materials and visual outputs used in evaluation.
Reproducibility Commitment¶
The repository structure, dataset documentation, serialized models, and evaluation reports are organized to enable:
- Replication of experiments
- Verification of performance metrics
- Inspection of modeling decisions
- Transparent review of ethical safeguards
This project is intended for educational and research purposes.
It does not provide medical diagnosis or clinical decision-making functionality.