Table 2.

Prediction Performance of ML Models

Prediction Model	AUC	P Value	Sensitivity	Specificity	PPV	NPV
Nonsevere exacerbation
Logistic regression	0.71 (0.70-0.72)	Reference	0.60 (0.59-0.62)	0.71 (0.70-0.72)	0.52 (0.51-0.54)	0.77 (0.76-0.78)
Random forest	0.68 (0.67-0.69)	< .01	0.60 (0.58-0.61)	0.67 (0.66-0.68)	0.49 (0.48-0.50)	0.76 (0.75-0.77)
LightGBM	0.71 (0.70-0.72)	.44	0.64 (0.62-0.65)	0.67 (0.66-0.68)	0.51 (0.49-0.52)	0.78 (0.77-0.78)
ED visit
Logistic regression	0.78 (0.76-0.80)	Reference	0.67 (0.62-0.71)	0.77 (0.76-0.77)	0.10 (0.09-0.11)	0.98 (0.98-0.99)
Random forest	0.84 (0.82-0.86)	.17	0.75 (0.71-0.79)	0.78 (0.77-0.79)	0.12 (0.11-0.13)	0.99 (0.98-0.99)
LightGBM	0.88 (0.86-0.89)	< .01	0.84 (0.81-0.88)	0.76 (0.75-0.77)	0.12 (0.11-0.13)	0.99 (0.99-0.99)
Hospitalization
Logistic regression	0.81 (0.77-0.84)	Reference	0.76 (0.70-0.82)	0.74 (0.73-0.74)	0.04 (0.04-0.05)	1 (0.99-1)
Random forest	0.79 (0.76-0.83)	.47	0.59 (0.52-0.66)	0.86 (0.85-0.87)	0.06 (0.05-0.07)	0.99 (0.99-0.99)
LightGBM	0.85 (0.82-0.88)	< .01	0.86 (0.81-0.91)	0.73 (0.72-0.73)	0.05 (0.04-0.05)	1 (1-1)

Data are presented as No. (%) or median (interquartile range). AUC = area under the receiver operating characteristic curve; LightGBM = light gradient boosting machine; ML = machine learning; NPV = negative predictive value; PPV = positive predictive value.