Search

PythonReactSEOPDF
OpenClaw

medical-entity-extractor

Extract medical entities (symptoms, medications, lab values, diagnoses) from patient messages.

binubmuse0 source installs☆ 0 Repository stars

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install binubmuse/medical-entity-extractor

Inspect the source and compatibility requirements, then follow its installation steps.

Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.

View source ↗Guides

Skill instructions

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": "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

[
  {
    "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 Lisino...

Source information

Source
github
Repository
openclaw/skills
Path
skills/binubmuse/medical-entity-extractor
Version
—
Commit
23569017ac3099270f7209207d94b873c32a4eeb
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# 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 Lisino...

Related skills

OpenClaw

geo-optimization

capt-marbles

Generative Engine Optimization (GEO) for AI search visibility. Optimize content to appear in ChatGPT, Perplexity, Claude, and Google AI Overviews. Use when optimizing websites, pages, or content for LLM discove...

OpenClaw

139mail

chang-tong

139邮箱邮件收发 Skill - 支持 IMAP/SMTP 协议,兼容139/QQ/163/Gmail等主流邮箱 功能: 1. 发送邮件(SMTP) 2. 接收邮件(IMAP) 3. 查看收件箱列表 4. 查看未读邮件 当用户提到 "发邮件"、"收邮件"、"查看邮件"、"邮箱"...