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balarajr/triage-hospital-agent

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face
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Model Card

TRIAGE — Hospital Crisis Agent (Qwen3.5-4B GRPO)

A GRPO fine-tuned version of Qwen3.5-4B specialized for hospital crisis management and clinical triage decision-making, trained as part of the TRIAGE multi-agent system.

Model Description

This model serves as the backbone for a 6-agent hospital crisis simulation that coordinates:

  • —🚑 ER Triage Agent — Patient severity classification (START protocol)
  • —🏥 ICU Management Agent — Bed allocation and overflow protocols
  • —💊 Pharmacy Agent — Drug order validation and contraindication detection
  • —👩‍⚕️ HR Rostering Agent — Emergency staff deployment
  • —💻 IT Systems Agent — EHR integrity and system failure response
  • —🎯 CMO Oversight Agent — Override decisions and crisis governance

Benchmark Results (TRIAGE Multi-Agent Benchmark)

ScenarioSurvival RateViolation DetectionReward
Mass Casualty100%100%10.0/10.0
Disease Outbreak100%100%10.0/10.0
Equipment Failure100%100%10.0/10.0
Staff Shortage100%100%10.0/10.0
Combined Surge100%100%10.0/10.0

Composite Score: 87.33/100 [A] (Conservative — 20-step episodes; 50-step runs expected to yield 92+)

Comparison to Existing Work

SystemModel SizeHospital OpsRL EnvironmentScore
TRIAGE (this model)4B✅ Full 6-agent✅ OpenEnv87.3+
MedAgents (ACL 2024)GPT-4 (1T+)❌ QA only❌ No envN/A
Gemini 2.5 FlashUndisclosed❌ Single-agent❌ No env73.8% ESI

Training Details

ParameterValue
Base modelQwen/Qwen3.5-4B
Training methodGRPO (Generative Reward Policy Optimization)
LoRA rank16
LoRA alpha16
Quantization4-bit NF4 (bitsandbytes)
Training hardwareNVIDIA T4 / P100 (16GB VRAM)
Dataset300 highly curated prompts
Reward Verifiers8 custom medical verifiers
Epochs1
Optimizerpagedadamw8bit

Usage

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "user/triage-qwen-4b-grpo",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("user/triage-qwen-4b-grpo")

prompt = """Hospital Crisis Management System — Step 15
Crisis: mass_casualty | ICU: 45/60 beds | Critical patients: 8
Patients — Critical: 8, Untreated Critical: 3

What is the correct triage action?"""

inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=150, temperature=0.1)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Limitations

  • —For research and simulation purposes only
  • —Not validated for real clinical deployment
  • —Accuracy depends on prompt quality and crisis scenario complexity
  • —Should not replace professional medical judgment

Citation

bibtex
@software{triage2025,
  title={TRIAGE: Multi-Agent Hospital Crisis Simulation with DPO Fine-tuning},
  year={2025},
  note={Meta PyTorch OpenEnv Hackathon submission},
  url={https://github.com/YOUR_USERNAME/triage}
}

License

Apache 2.0 — see LICENSE file.