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. Author manuscript; available in PMC: 2016 Nov 1.
Published in final edited form as: Pediatr Crit Care Med. 2015 Nov;16(9):e332–e339. doi: 10.1097/PCC.0000000000000560

Figure 1.

Figure 1

Four classes of data were combined into five feature sets. Each feature set was used to train and test cardiac arrest prediction models using four modeling algorithms. Of the 1025 variables, 20 were multivariate, 497 were raw time series, 288 were clinical calculations, and 220 were time series trend analysis. Differential performance between the Multivariate feature set with the Linear Regression algorithm and other models measured: 1) effects attributable to data class (for a given algorithm); or 2) effects attributable to modeling algorithm (for a given data class).