Table 2.

Performance comparison of the five machine learning classification models used

	Conditional random forest (M1)	Random forest (M2)	Ordinal forest (M3)	Partitional tree (M4)	Conditional inference tree (M5)
Accuracy	0.70 (0.54–0.83)	0.79 (0.64–0.90)	0.77 (0.61–0.88)	0.65 (0.49–0.79)	0.79 (0.64–0.90)
Sensitivity
Discharge	1.00	0.97	0.87	0.77	0.97
Decease	0.00	0.63	0.75	0.50	0.63
ICU	0.00	0.00	0.20	0.20	0.00
Specificity
Discharge	0.00	0.38	0.54	0.62	0.38
Decease	1.00	0.97	0.89	0.74	0.97
ICU	1.00	1.00	1.00	0.97	1.00
PPV
Discharge	0.70	0.78	0.81	0.82	0.78
Decease	NaN	0.83	0.60	0.31	0.83
ICU	NaN	NaN	1.00	0.50	NaN
NPV
Discharge	NA	0.83	0.63	0.53	0.83
Decease	0.81	0.92	0.94	0.87	0.92
ICU	0.88	0.88	0.90	0.90	0.88
Detection rate
Discharge	0.70	0.67	0.60	0.53	0.67
Decease	0.00	0.12	0.14	0.09	0.12
ICU	0.00	0.00	0.02	0.02	0.00

ICU, intensive care unit; NaN, not a number (one or more values is a 0); NPV, negative predictive value; PPV, positive predictive value.