Ehr Semantic Compressor

Usage

Basic Usage

python scripts/main.py --input ehr_document.txt --output summary.json

Input Format

{
  "ehr_text": "Full EHR document text...",
  "max_length": 300,
  "extract_sections": ["allergies", "medications", "diagnoses", "family_history"]
}

Output Format

{
  "status": "success",
  "data": {
    "summary": "Structured bullet-point summary...",
    "extracted_sections": {
      "allergies": [...],
      "medications": [...],
      "diagnoses": [...],
      "family_history": [...]
    },
    "metadata": {
      "original_length": 2500,
      "summary_length": 280,
      "compression_ratio": 0.89
    }
  }
}

Parameters

Technical Details

Architecture

• Base Model: Transformer-based encoder-decoder architecture
• Medical Domain Adaptation: Fine-tuned on clinical text corpora
• Section Extraction: Rule-based + ML hybrid approach for structured data
• Processing Pipeline: Text segmentation -> Summarization -> Section extraction -> Output formatting

Dependencies

See references/requirements.txt for complete list.

Key dependencies:

• transformers >= 4.30.0
• torch >= 2.0.0
• spacy >= 3.6.0
• scispacy >= 0.5.3

Performance

• Processing Time: 10-20 seconds for 1600+ word documents
• Memory: Requires ~2GB RAM
• Output Length: 200-300 words (configurable)
• Compression Ratio: ~85-90%

References

• references/requirements.txt - Python dependencies
• references/guidelines.md - Clinical summarization guidelines
• references/sample_input.json - Example input format
• references/sample_output.json - Example output format

Safety & Compliance

• No external API calls or service dependencies
• All processing performed locally
• No patient data transmitted outside the system
• Error messages are semantic and do not expose technical details

Testing

Run unit tests:

cd scripts
python test_main.py

Error Handling

All errors return semantic messages:

{
  "status": "error",
  "error": {
    "type": "input_validation_error",
    "message": "EHR text is empty or too short",
    "suggestion": "Provide EHR text with at least 100 words"
  }
}

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support