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. 2019 Aug 16:7:44.
doi: 10.1186/s40560-019-0393-1. eCollection 2019.

Artificial neural networks improve and simplify intensive care mortality prognostication: a national cohort study of 217,289 first-time intensive care unit admissions

Gustav Holmgren  1 , Peder Andersson  2   3 , Andreas Jakobsson  1 , Attila Frigyesi  1   2   3
Affiliations

Artificial neural networks improve and simplify intensive care mortality prognostication: a national cohort study of 217,289 first-time intensive care unit admissions

Gustav Holmgren et al. J Intensive Care. .

Abstract

Purpose: We investigated if early intensive care unit (ICU) scoring with the Simplified Acute Physiology Score (SAPS 3) could be improved using artificial neural networks (ANNs).

Methods: All first-time adult intensive care admissions in Sweden during 2009-2017 were included. A test set was set aside for validation. We trained ANNs with two hidden layers with random hyper-parameters and retained the best ANN, determined using cross-validation. The ANNs were constructed using the same parameters as in the SAPS 3 model. The performance was assessed with the area under the receiver operating characteristic curve (AUC) and Brier score.

Results: A total of 217,289 admissions were included. The developed ANN (AUC 0.89 and Brier score 0.096) was found to be superior (p <10-15 for AUC and p <10-5 for Brier score) in early prediction of 30-day mortality for intensive care patients when compared with SAPS 3 (AUC 0.85 and Brier score 0.109). In addition, a simple, eight-parameter ANN model was found to perform just as well as SAPS 3, but with better calibration (AUC 0.85 and and Brier score 0.106, p <10-5). Furthermore, the ANN model was superior in correcting mortality for age.

Conclusion: ANNs can outperform the SAPS 3 model for early prediction of 30-day mortality for intensive care patients.

Keywords: Artificial intelligence; Artificial neural networks; Critical care; Intensive care; Machine learning; Mortality; Prediction; Survival.

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Conflict of interest statement

Competing interestsThe authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
ANN. A schematic artificial neural network (ANN) with two hidden layers and a single neuron output
Fig. 2
Fig. 2
ROC. Receiver operating characteristic (ROC) curve for the artificial neural network (ANN) model and Simplified Acute Physiology Score (SAPS 3) model showed improved area under curve (AUC)
Fig. 3
Fig. 3
Calibration. Calibration curves (observed mortality ratio (OMR) versus expected mortality ratio (EMR)) for the Simplified Acute Physiology Score (SAPS 3) model and the artificial neural network (ANN) model demonstrated improved calibration (Brier score 0.096 vs. 0.110, p <10−5) in the high EMR range (0.7–1) for the ANN model
Fig. 4
Fig. 4
Age. Standardised mortality ratio (SMR) as a function of age for the Simplified Acute Physiology Score (SAPS 3) model (left panel) and the artificial neural network (ANN) model (right panel) for the test set (n = 36,214). The ANN model was superior in correcting for age as a prognostic factor (the single most important prognostic factor) as compared to SAPS 3. SMR is shown with a 95% confidence interval
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