medical-entity-extractor
Extract medical entities (symptoms, medications, lab values, diagnoses) from patient messages.
Best use case
medical-entity-extractor is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Extract medical entities (symptoms, medications, lab values, diagnoses) from patient messages.
Teams using medical-entity-extractor should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/medical-entity-extractor/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How medical-entity-extractor Compares
| Feature / Agent | medical-entity-extractor | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Extract medical entities (symptoms, medications, lab values, diagnoses) from patient messages.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
```json
[
{
"id": "msg-123",
"priority_score": 78,
"priority_bucket": "P1",
"subject": "Medication side effects",
"from": "patient@example.com",
"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
```json
[
{
"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:
```bash
openclaw skill run medical-entity-extractor --input '[{"id":"msg-1","priority_score":78,...}]' --json
```
Or programmatically:
```typescript
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)
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