MedGemma 4B — Emergency Severity Index (ESI) Triage (GRPO + SFT, Merged)

A fine-tuned variant of google/medgemma-4b-it for emergency department triage. Given a triage narrative (chief complaint, vitals, history, arrival mode), the model classifies cases into ESI levels 1–5 with structured chain-of-thought reasoning.

⚠️ Research model. Not approved for clinical use. Not a substitute for clinician judgment. This model inherits Google's Gemma Terms of Use and the HAI-DEF terms from medgemma. Research and development use only.

Result

83.3% exact / 97.2% adjacent on the 36-case MIETIC expert-annotated evaluation set.

A 4 B model beating a 9 B model by 5.5 pp — the combination of MedGemma's medical pretraining + structured SFT + rule-aware GRPO is unusually effective on this task.

Output format

EXTRACTION:
- Chief complaint: ...
- Vital signs: ...
- Red flags: ...

ESI ALGORITHM:
- Step A (lifesaving): ...
- Step B (high-risk): ...
- Step C (resources): ...
- Step D (vitals): ...

ANSWER: ESI 2

Training pipeline

Stage 1 — Supervised Fine-Tuning (SFT)

• Base: google/medgemma-4b-it
• Data: bert_training_v43_balanced.jsonl (7,483 records, class-balanced across ESI 1–5)
  • MIMIC-IV-ED gold (engine-verified) + MIETIC narrative cases
• LoRA: r=32, α=32, target modules = q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Hyperparams: 3 epochs, LR=2e-4 (cosine), batch=4, grad_accum=4, max_seq_length=2048
• Final loss: ~0.20 (converged by epoch 2)
• Hardware: NVIDIA GB10, ~2.8 h
• Eval result: 77.8–80.6% exact on MIETIC-36

Stage 2 — GRPO with rule-aware reward

The SFT model already matched Qwen v49's 9B GRPO result, but failed on the same patterns Qwen v46 missed (intubation/lifesaving intervention rule, severe pain rule, open injury). v49's rule-aware reward was applied to medgemma to fix those specific failures.

Reward function:

Hyperparams:

• Warm-start: medgemma-sft-v1 LoRA
• 300 steps, LR=2e-7 (cosine), G=8 generations/step
• max_new_tokens = 1024
• ESI-1 oversample 5×, ESI-2 oversample 3×
• attn_implementation=eager (workaround for SDPA dtype mismatch in Gemma3+GRPO)
• Hardware: NVIDIA GB10, ~19.3 h

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

repo = "vadimbelsky/medgemma-4b-esi-triage-grpo-v1"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map="auto")

SYSTEM = (
    "You are an expert emergency triage nurse. "
    "First extract clinical fields from the triage note, "
    "then apply the ESI algorithm step by step, then state the ESI level."
)

case = ("A 67-year-old male arrived via ambulance with sudden onset chest pain "
        "radiating to the left arm, diaphoresis, and shortness of breath. "
        "BP 88/60, HR 118, RR 24, SpO2 91%. History of MI and hypertension. Pain 9/10.")

prompt = tokenizer.apply_chat_template(
    [{"role": "system", "content": SYSTEM},
     {"role": "user",   "content": case}],
    tokenize=False, add_generation_prompt=True,
)
out = model.generate(
    **tokenizer(prompt, return_tensors="pt").to(model.device),
    max_new_tokens=1024, temperature=0.1, do_sample=True,
)
print(tokenizer.decode(out[0], skip_special_tokens=True))

Per-class results (MIETIC-36)

ESI-4 and ESI-5 are perfect on this eval. The one remaining adjacency violation is a single ESI-1 case where the model predicted ESI-3 — the patient's narrative didn't match any of our four rule-bonus trigger regexes.

Known limitations

• Small eval set: 36 cases. The 95% CI for 83.3% is roughly ±12 pp. Treat the headline number as directional.
• Rule bonuses are pattern-based: regex triggers for intubation, lifesaving intervention, open injury, severe pain. Cases requiring clinical rules outside these patterns may still err.
• Single dangerous error remains: 1/36 case (~3%) where a clinical ESI-1 is misclassified to ESI-3. Mitigation requires either broader rule coverage or larger training data.
• English-only: trained on English narratives.
• MIMIC-IV-ED distribution: reflects U.S. emergency-department practice and retrospective acuity assignments.

Citation and acknowledgments

• Base model: google/medgemma-4b-it
• Data: MIMIC-IV-ED (gold ESI labels) + MIETIC narrative corpus
• Training stack: Unsloth FastModel + TRL GRPOTrainer (with patches for Gemma3 GRPO compatibility — see model repo for details)
• Developed at ScienceSoft as part of a medical SLM research initiative

This is a modified version of MedGemma. The base model is Google's, used under the Gemma Terms of Use. Modifications: SFT + GRPO fine-tuning for ESI triage. The HAI-DEF research-only restriction is preserved.