---
tags:
- ml-intern
---
# GVHD Severity Prediction - Final Results

**Best Model: Stacking Ensemble (CatBoost + XGBoost + Neural Net)**

| Metric | Value |
|--------|-------|
| **AUC** | **0.7083 ± 0.0117** |
| Baseline AUC | 0.7034 |
| Improvement | +0.0049 (+0.7%) |
| Brier (Platt Calibrated) | 0.2019 |
| Optimal Threshold | 0.544 |
| Sensitivity | 68.4% |
| Specificity | 58.8% |
| PPV | 76.9% |
| NPV | 48.3% |

## Model Comparison

| Model | AUC Mean | AUC Std | Fold1 | Fold2 | Fold3 | Fold4 | Fold5 |
|-------|----------|---------|-------|-------|-------|-------|-------|
| CatBoost | 0.6963 | ±0.0105 | 0.700 | 0.711 | 0.699 | 0.679 | 0.694 |
| XGBoost | 0.6986 | ±0.0126 | 0.705 | 0.711 | 0.704 | 0.675 | 0.698 |
| NeuralNet | 0.6870 | ±0.0088 | 0.692 | 0.699 | 0.698 | 0.677 | 0.681 |
| **Stacking** | **0.7083** | **±0.0117** | **0.714** | **0.722** | **0.714** | **0.688** | **0.703** |

## Calibration

| Method | Brier Score |
|--------|------------|
| Raw | 0.2150 |
| Platt Scaling | **0.2019** |
| Isotonic Regression | 0.2024 |

## Key Improvements

1. **Feature Engineering**: interactions, polynomials, log transforms, missingness indicators
2. **GPU Acceleration**: Tesla T4 for CatBoost and Neural Net
3. **Stacking Ensemble**: Logistic Regression meta-learner on OOF predictions
4. **Probability Calibration**: Platt scaling for clinical deployment

## Files

- `gvhd_gpu_pipeline.py` - Complete pipeline code (every line commented)
- `result_comparison_final.csv` - Model comparison table
- `GVHD_Final_Report.ipynb` - Jupyter notebook with tables
- `calibration_plot.png` - Calibration curve

## Honest Ceiling

Pre-transplant-only models plateau at **AUC ≈ 0.71**. Any claim > 0.75 requires post-transplant biomarkers (Day 7-14).

<!-- ml-intern-provenance -->
## Generated by ML Intern

This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub.

- Try ML Intern: https://smolagents-ml-intern.hf.space
- Source code: https://github.com/huggingface/ml-intern

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "cuimiandashi/gvhd-analysis"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
```

For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.