medical-entity-extractor

name	medical-entity-extractor
description	Extract medical entities (symptoms, medications, lab values, diagnoses) from patient messages.

Medical Entity Extractor

Extract structured medical information from unstructured patient messages.

What This Skill Does

1. Symptom Extraction: Identifies symptoms, severity, duration, and progression
2. Medication Extraction: Finds medication names, dosages, frequencies, and side effects
3. Lab Value Extraction: Parses lab results, vital signs, and measurements
4. Diagnosis Extraction: Identifies mentioned diagnoses and conditions
5. Temporal Extraction: Captures when symptoms started, how long they've lasted
6. Action Items: Identifies requested actions (appointments, refills, questions)

Input Format

[
  {
    "id": "msg-123",
    "priority_score": 78,
    "priority_bucket": "P1",
    "subject": "Medication side effects",
    "from": "[e-mail]",
    "date": "2026-02-27T10:30:00Z",
    "body": "I've been feeling dizzy since starting the new blood pressure medication (Lisinopril 10mg) three days ago. My BP this morning was 145/92."
  }
]

Output Format

[
  {
    "id": "msg-123",
    "entities": {
      "symptoms": [
        {
          "name": "dizziness",
          "severity": "moderate",
          "duration": "3 days",
          "onset": "since starting new medication"
        }
      ],
      "medications": [
        {
          "name": "Lisinopril",
          "dosage": "10mg",
          "frequency": null,
          "context": "new medication"
        }
      ],
      "lab_values": 
        
           
           
           
           
        
      
       
        
           
           
        
      
       
        
           
           
        
      
    
     
  

Entity Types

Symptoms

• Name, severity (mild/moderate/severe), duration, onset, progression (improving/stable/worsening)

Medications

• Name, dosage, frequency, route, context (new/existing/stopped)

Lab Values

• Type (BP, glucose, cholesterol, etc.), value, unit, timestamp, normal range

Diagnoses

• Name, context (confirmed/suspected/ruled out)

Vital Signs

• Temperature, heart rate, respiratory rate, oxygen saturation, blood pressure

Action Items

• Type (appointment, refill, question, callback), urgency, reason

Medical Terminology Handling

The skill recognizes:

• Common abbreviations (BP, HR, RR, O2 sat, etc.)
• Brand and generic medication names
• Lay terms for medical conditions ("sugar" → diabetes, "heart attack" → MI)
• Temporal expressions ("since yesterday", "for the past week")

Integration

This skill can be invoked via the OpenClaw CLI:

openclaw skill run medical-entity-extractor --input '[{"id":"msg-1","priority_score":78,...}]' --json

Or programmatically:

const result = await execFileAsync('openclaw', [
  'skill', 'run', 'medical-entity-extractor',
  '--input', JSON.stringify(scoredMessages),
  '--json'
]);

Recommended Model: Claude Sonnet 4.5 (openclaw models set anthropic/claude-sonnet-4-5)

Privacy & Security

• All processing happens locally via OpenClaw
• No data is sent to external services (except Claude API for LLM processing)
• Extracted entities remain in your local environment