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BytesAgain β€Ί Skills β€Ί Ehr Semantic Compressor
πŸ¦€ ClawHub

Ehr Semantic Compressor

by @aipoch-ai

AI-powered EHR summarization using Transformer architecture to extract key clinical information from lengthy medical records

Versionv0.1.0
Downloads736
#legal#health
TERMINAL
clawhub install ehr-semantic-compressor

πŸ“– About This Skill

name: ehr-semantic-compressor description: AI-powered EHR summarization using Transformer architecture to extract key clinical information from lengthy medical records version: 1.0.0 category: Clinical tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06'

EHR Semantic Compressor

Overview

AI-powered EHR summarization using Transformer architecture to extract key clinical information from lengthy medical records. This skill processes lengthy Electronic Health Record (EHR) documents and generates structured, clinically accurate summaries.

Technical Difficulty: High

When to Use

  • Input contains lengthy EHR documents (1600+ words) requiring summarization
  • Clinical records need structured extraction of key information
  • Quick review of patient history, medications, allergies, or diagnoses is needed
  • Medical documentation requires compression while maintaining accuracy

    • Core Features

    1. Fast Processing: Process lengthy EHR documents (1600+ words) in 10-20 seconds 2. Structured Summaries: Generate bullet-point summaries (200-300 words) 3. Critical Information Extraction: - Patient allergies and adverse reactions - Family medical history - Current and past medications - Diagnoses and conditions - Vital signs and lab results - Procedures and surgeries 4. Clinical Accuracy: Maintains completeness of medical information

    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

    | Parameter | Type | Default | Required | Description | |-----------|------|---------|----------|-------------| | --input, -i | string | - | Yes | Input EHR document text file path | | --output, -o | string | - | No | Output JSON file path | | --max-length | int | 300 | No | Maximum summary length in words | | --extract-sections | string | all | No | Comma-separated sections to extract | | --format | string | json | No | Output format (json, markdown, text) |

    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

    | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low |

    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 β†’ Expected output 2. Edge Case: Invalid input β†’ Graceful error handling 3. Performance: Large dataset β†’ Acceptable processing time

    Lifecycle Status

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

    ⚑ When to Use

    TriggerAction
    - Clinical records need structured extraction of key information
    - Quick review of patient history, medications, allergies, or diagnoses is needed
    - Medical documentation requires compression while maintaining accuracy

    πŸ’‘ Examples

    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
    }
  }
}

    βš™οΈ Configuration

    # Python dependencies
pip install -r requirements.txt