# DeepHealth

## Evaluation

This repo includes two event-driven evaluation entrypoints:

- `evaluate_next_event.py`: next-event prediction using short-window CIF
- `evaluate_horizon.py`: horizon-capture evaluation using CIF at multiple horizons

### IMPORTANT metric disclaimers

- **AUC** reported by `evaluate_horizon.py` is “time-dependent” only because the label depends on the chosen horizon $\tau$.
	Without explicit follow-up end times / censoring, this is **not** a classical risk-set AUC with IPCW.
	Use it for **model comparison and diagnostics**, not strict statistical interpretation.

- **Brier score** reported by `evaluate_horizon.py` is an unadjusted diagnostic/proxy metric (no censoring adjustment).
	Use it to detect probability-mass compression / numerical stability issues; do not claim calibrated absolute risk.

### Example

```bash
# Next-event (no --horizons)
python evaluate_next_event.py \
	--run_dir runs/your_run \
	--tau_short 0.25 \
	--age_bins 40 45 50 55 60 65 70 inf \
	--device cuda \
	--batch_size 256 \
	--seed 0

# Horizon-capture
python evaluate_horizon.py \
	--run_dir runs/your_run \
	--horizons 0.25 0.5 1.0 2.0 5.0 10.0 \
	--age_bins 40 45 50 55 60 65 70 inf \
	--device cuda \
	--batch_size 256 \
	--seed 0
```