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

# 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