Ehr Semantic Compressor

Source: https://github.com/aipoch/medical-research-skills

EHR Semantic Compressor

When to Use

• Use this skill when the task needs 1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work. 2. Validate that the request matches the documented scope and stop early if the task would require unsupported as.
• Use this skill for academic writing tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

• Scope-focused workflow aligned to: 1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work. 2. Validate that the request matches the documented scope and stop early if the task would require unsupported as.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

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

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Academic Writing/ehr-semantic-compressor"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py --input "Audit validation sample with explicit symptoms, history, assessment, and next-step plan."

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

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

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

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

Performance

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