Medical entity extractor
Skill BioTender-max/awesome-bio-agent-skills/skills/openclaw/medical-entity-extractor
A curated collection of AI agent skills for biomedical research, covering genomics, proteomics, single-cell analysis, clinical AI, and protein design.
npx -y skills add BioTender-max/awesome-bio-agent-skills --skill medical-entity-extractorAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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Extract medical entities (symptoms, medications, lab values, diagnoses) from patient messages.
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SKILL.md
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Medical Entity Extractor
Extract structured medical information from unstructured patient messages.
What This Skill Does
- Symptom Extraction: Identifies symptoms, severity, duration, and progression
- Medication Extraction: Finds medication names, dosages, frequencies, and side effects
- Lab Value Extraction: Parses lab results, vital signs, and measurements
- Diagnosis Extraction: Identifies mentioned diagnoses and conditions
- Temporal Extraction: Captures when symptoms started, how long they've lasted
- Action Items: Identifies requested actions (appointments, refills, questions)
Input Format
[
{
"id": "msg-123",
"priority_score": 78,
"priority_bucket": "P1",
"subject": "Medication side effects",
"from": "[email protected]",
"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": [
{
"type": "blood_pressure",
"value": "145/92",
"unit": "mmHg",
"timestamp": "this morning"
}
],
"diagnoses": [
{
"name": "hypertension",
"context": "implied by blood pressure medication"
}
],
"action_items": [
{
"type": "medication_review",
"reason": "possible side effect (dizziness)"
}
]
},
"summary": "Patient reports dizziness after starting Lisinopril 10mg 3 days ago. BP elevated at 145/92. Possible medication side effect requiring review."
}
]
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