SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Input Modality	Diagnostic	Prognostic
Image	EHR	PE	In-Hospital Mortality	Re-admission	PH
CT	M	G	(+)	1 m	6 m	12 m	1 m	6 m	12 m	12 m
✓	0.278	0.423	0.369	0.265	0.136	0.347	0.346	0.325
✓	0.026	0.011	0.010	0.015	0.008	0.004	0.007	0.012
✓	0.015	0.020	0.053	0.017	0.016	0.016	0.026	0.021
✓	✓	0.024	0.004	0.012	0.017	0.009	0.009	0.010	0.016
✓	✓	0.015	0.007	0.019	0.017	0.004	0.009	0.014	0.023
✓	✓	0.007	0.004	0.010	0.014	0.006	0.009	0.014	0.009
✓	✓	✓	0.016	0.005	0.011	0.015	0.006	0.009	0.013	0.025
Table 23: The calibration performance in ECE for our different baseline modeling strategies, including late fusion. Lower scores indicate better models with this metric. CT is the CTPA based LRCN model, M is the structured EHR based MOTOR model, and G is the structured EHR based gradient-boosted trees model. The best overall models are bolded and the best individual models are underlined.