Table 1.

Measures of model performance in validation data set. Mean performance of modeling algorithm (rows) or feature set (columns) is listed outside the table, with the best measure in boldface. Inside the table, reference performance for MV feature + LR algorithm is shown in the top left, underlined. Models using trend calculations without raw time series features (the TRD data class) along with the SVM algorithm demonstrated the best overall performance. Removing single measurement variables not subject to trend calculations did not significantly impact model performance.

Matrix of Model Performance in Validation Data
All Available Relevant Variables
Accuracy	MV	TS	CC	TRD	ALL
LR	78%	69%	58%	77%	69%	70%
DT	72%	88%	80%	81%	86%	81%
NN	67%	63%	67%	72%	66%	67%
SVM	77%	89%	88%	94%	88%	87%
	73%	77%	73%	81%	77%
AUROC	MV	TS	CC	TRD	ALL
LR	87%	72%	60%	72%	80%	74%
DT	74%	90%	79%	83%	88%	83%
NN	82%	83%	80%	92%	79%	83%
SVM	82%	95%	96%	98%	97%	94%
	81%	85%	79%	86%	86%

Variables Constrained to Trend Calculation Compatible
Accuracy	MV	TS	CC	TRD	ALL
LR	83%	78%	----	87%	84%	83%
DT	81%	81%	----	81%	86%	82%
NN	64%	56%	----	72%	64%	64%
SVM	73%	84%	----	94%	91%	86%
	75%	75%	----	84%	81%
AUROC	MV	TS	CC	TRD	ALL
LR	89%	79%	----	87%	83%	85%
DT	81%	81%	----	80%	86%	82%
NN	79%	74%	----	92%	82%	82%
SVM	83%	91%	----	98%	97%	92%
	83%	81%	----	89%	87%

Abbreviations:

AUROC = area under the receiver operating characteristic curve.

Data Classes:

MV = multivariate

TS = multivariate + time series

CC = multivariate + time series + clinical calculations

TRD = multivariate + trend analysis

ALL = all 1025 variables - 17

Model classes:

LR = linear regression

DT = decision tree

NN = neural network

SVM = support vector machine