README.md

metadata

title: MedTriage OpenEnv
emoji: 🏥
sdk: docker
pinned: false
app_port: 7860
tags:
  - openenv
  - healthcare
  - ai-agents
base_path: /web

MedTriage OpenEnv

A real-world medical triage simulation environment built for the Meta PyTorch OpenEnv Hackathon. This environment allows AI agents to learn how to categorize patient symptoms into appropriate clinical triage levels using the standard OpenEnv API.

📋 Environment Overview

MedTriage simulates the decision-making process of a clinical triage officer. The agent receives patient demographics, vitals, and unstructured symptom text, and must decide on the safest and most efficient path for care.

🎯 Real-World Utility

In real healthcare settings, accurate triage is critical for:

1. Patient Safety: Ensuring life-threatening conditions (like heart attacks) are seen immediately.
2. Resource Optimization: Preventing hospital ERs from being overwhelmed by minor cases that can be treated at home.

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🎮 Action Space

The agent interacts via the triage_patient tool:

• level: (IntEnum)
  • 0: Self-Care (Over-the-counter/rest)
  • 1: Clinic (Primary Care appointment in 24-48h)
  • 2: Urgent Care (Same-day care)
  • 3: Emergency (Immediate ER/Ambulance)
• reasoning: (String) A medical justification for the triage level.

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📥 Observation Space

Each observation provides:

• patient_id: Unique identifier.
• age / gender: Basic demographics.
• symptoms_text: Unstructured description of the patient's complaint.
• vitals: Dictionary containing temp, bp (Blood Pressure), hr (Heart Rate), and spo2 (Oxygen).
• history: List of prior medical conditions or medications.

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🚀 Tasks & Difficulty

The environment includes 3 built-in tasks with automated graders:

Task ID	Name	Difficulty	Ground Truth
TASK_EASY	Seasonal Allergies	Easy	Self-Care (0)
TASK_MEDIUM	Possible Appendicitis	Medium	Urgent Care (2)
TASK_HARD	Atypical MI	Hard	Emergency (3)

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📈 Reward Function (Grader)

Scores range from 0.0 to 1.0:

• 1.0: Perfect match with ground truth.
• 0.5: Over-triage (Safe but resource-intensive).
• 0.2: Minor under-triage.
• 0.0: Dangerous under-triage (e.g., sending a heart attack to self-care).

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🛠️ Setup & Usage

Local Development

1. Install Dependencies:

   pip install -e .
   

2. Start the Server:

   python server/app.py
   

3. Run Baseline:

   python inference.py
   

Docker

docker build -t med-triage-env:latest .
docker run -p 8002:8002 med-triage-env:latest

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🌐 API Endpoints

• /tasks: List all available tasks.
• /baseline: Run the baseline inference.
• /grader: Get the score of the last episode.
• /health: Environment health check.