ClinicalGen

ClinicalGen aims to streamline the process of generating comprehensive data quality reports tailored for clinical data. By automating the evaluation of data integrity and presenting insights in a clear, actionable format, ClinicalGen empowers researchers and healthcare professionals to enhance data reliability and improve clinical outcomes.

For Users

Overview

ClinicalGen is hosted at clinicalgen.straive.app. It offers:

1. Data Quality Report/Regulatory Report Generation: Generate a Data Quality report/Regulatory report from pre-loaded data.
2. Custom Report Generation: Upload your own Excel report for analysis.

How to Use

1. Visit clinicalgen.straive.app
2. Log in to LLM Foundry if prompted
3. Choose one of the following options:
   • Click "Generate" under "Data Quality Report" or "Regulatory Report" to use pre-loaded data
   • Click "Upload your Excel file here" under "Custom Report" to use your own data
4. View the generated report, including:
   • Executive Summary
   • Analysis of data quality
   • AI-generated recommendations

Features

• Interactive charts for bandwidth and session usage
• AI-powered recommendations based on report data
• Responsive design for various screen sizes
• Dark mode toggle

For Developers

Project Structure

• index.html: Main HTML file
• script.js: Core JavaScript functionality
• img/: Directory for images used in the report

Adding New Demos

To add a new demo:

1. Update the #demos section in index.html
2. Create a new Excel file (e.g., new-demo.xlsx) with the required data structure:
   • Include a "Summary" sheet with key-value pairs
   • Add additional sheets for specific data sections
3. Update the qualityReport function in script.js to handle the new data structure if needed
4. Modify the chart generation code if the new demo requires different visualizations

Technologies Used

• HTML5, CSS3 (Bootstrap 5.3.3)
• JavaScript (ES6+)
• lit-html for templating
• Chart.js for data visualization
• XLSX library for Excel file parsing
• Marked for Markdown parsing
• LLM Foundry API for AI-generated recommendations

Development Setup

1. Clone the repository
2. Serve the project using a local web server (e.g., python -m http.server)
3. Open http://localhost:8000 in your browser

API Integration

The project uses the LLM Foundry API for generating recommendations. Ensure you have the necessary credentials and update the token retrieval logic in script.js if needed.

Customization

• Modify the qualityReport function in script.js to change the report structure
• Update styles in index.html to customize the appearance
• Adjust chart configurations in script.js for different visualizations