TRIAGE: Dialectical Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series with LLMs • 5 items • Updated
TRIAGE-4B-P12-SFT
SFT checkpoints released as part of the work, applied to the P12 dataset.
The model was presented in the paper TRIAGE: Dialectical Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series with LLMs.
Each split was fine-tuned from Qwen/Qwen3-4B-Base and placed in its own split_N/ subfolder; per-split checkpoints were selected by Validation AUPRC over a 3-epoch SFT run.
Code, data, & training pipeline: https://github.com/HyeongWon-Jang/TRIAGE
Quick start
from transformers import AutoModelForCausalLM, AutoTokenizer
split = "split_1" # one of split_1 ... split_5
repo = "Hyeongwon/TRIAGE-4B-P12-SFT"
tokenizer = AutoTokenizer.from_pretrained(repo, subfolder=split)
model = AutoModelForCausalLM.from_pretrained(repo, subfolder=split, device_map="auto")
The model expects a task-specific input/output template; for the full inference pipeline, see the linked GitHub repository.
Data
- Raw: PhysioNet Mortality Prediction Challenge 2012
- Processed (Raindrop, CC BY 4.0): figshare DOI
Further preprocessing and split-construction details are in the linked GitHub repository.
Framework versions
- Transformers: 4.57.3
- PyTorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.22.2
License
Model checkpoints are released under CC BY-NC 4.0 (non-commercial). Datasets remain under their respective licenses.
Model tree for Hyeongwon/TRIAGE-4B-P12-SFT
Base model
Qwen/Qwen3-4B-Base