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Table 3 (a) Performance of the models for the overall patient dataset, by also including variables related to kidney and liver function, (b) Performance of the models for the subgroup of patients admitted with respiratory disorders, by also including variables related to kidney and liver function

From: Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation

 

AUC

AP

PPV

NPV

MCC

(a) Overall cohort

LR

0.72 ± 0.02

0.57 ± 0.03

0.58 ± 0.03

0.78 ± 0.01

0.34 ± 0.03

RF

0.76 ± 0.02

0.63 ± 0.02

0.59 ± 0.04

0.80 ± 0.01

0.38 ± 0.03

LSTM

0.79 ± 0.02

0.68 ± 0.02

0.59 ± 0.04

0.83 ± 0.01

0.42 ± 0.04

(b) Cohort admitted with respiratory disorders

LR

0.73 ± 0.01

0.61 ± 0.01

0.58 ± 0.03

0.77 ± 0.02

0.35 ± 0.03

RF

0.78 ± 0.02

0.69 ± 0.04

0.61 ± 0.05

0.80 ± 0.02

0.41 ± 0.06

LSTM

0.79 ± 0.01

0.72 ± 0.02

0.63 ± 0.04

0.80 ± 0.01

0.43 ± 0.03

  1. Highest performance is shown in bold