Triage Hospital Agent

--- language: - en license: apache-2.0 base_model: Qwen/Qwen3.5-4B tags: - medical - triage - hospital - multi-agent - grpo - lora - qwen - clinical-ai - crisis-management datasets: - openlifescienceai/medmcqa - bigbio/med_qa pipeline_tag: text-generation --- # 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) | Scenario | Survival Rate | Violation Detection | Reward | |---|---|---|---| | Mass Casualty | 100% | 100% | 10.0/10.0 | | Disease Outbreak | 100% | 100% | 10.0/10.0 | | Equipment Failure | 100% | 100% | 10.0/10.0 | | Staff Shortage | 100% | 100% | 10.0/10.0 | | Combined Surge | 100% | 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 | System | Model Size | Hospital Ops | RL Environment | Score | |---|---|---|---|---| | **TRIAGE (this model)** | **4B** | **✅ Full 6-agent** | **✅ OpenEnv** | **87.3+** | | MedAgents (ACL 2024) | GPT-4 (1T+) | ❌ QA only | ❌ No env | N/A | | Gemini 2.5 Flash | Undisclosed | ❌ Single-agent | ❌ No env | 73.8% ESI | ## Training Details | Parameter | Value | |---|---| | Base model | Qwen/Qwen3.5-4B | | Training method | GRPO (Generative Reward Policy Optimization) | | LoRA rank | 16 | | LoRA alpha | 16 | | Quantization | 4-bit NF4 (bitsandbytes) | | Training hardware | NVIDIA T4 / P100 (16GB VRAM) | | Dataset | 300 highly curated prompts | | Reward Verifiers | 8 custom medical verifiers | | Epochs | 1 | | Optimizer | paged_adamw_8bit | ## 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.