Table 2. Describes the four models that were used to train and test the ML tool.

Table legend			AUROC (%)
	Actual Good Outcome	Actual Good Outcome	Training model: %
Actual Good Outcome	TN	FP
			Test model: %
Actual Bad Outcome	FN	TP
Model 1 –Only signs and symptoms, 31 variables			Model 1
	Actual Good Outcome	Actual Good Outcome	Training model: 89%
Actual Good Outcome	74—TN	42 FP
			Test model: 69%
Actual Bad Outcome	56 FN	95 TP
Model 2 –Laboratory biomarkers, 39 variables			Model 2
	Predicted Good Outcome	Predicted Bad Outcome	Training model: 97%
Actual Good Outcome	89	27
			Test model: 83%
Actual Bad Outcome	39	112
Model 3 –Extended mixed model, 91 variables			Model 3
	Predicted Good Outcome	Predicted Bad Outcome	Training model: 99%
Actual Good Outcome	95	21
			Test model: 85%
Actual Bad Outcome	40	111
Model 4 –Boosted mixed model, 20 variables			Model 4
	Predicted Good Outcome	Predicted Bad Outcome	Training model: 98%
Actual Good Outcome	87	29
			Test model: 84%
Actual Bad Outcome	40	111