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

Journal of Intensive Care

Gustav Holmgren¹  Attila Frigyesi¹  Andreas Jakobsson¹  Peder Andersson² 

[1] Centre for Mathematical Sciences, Mathematical Statistics, Lund University;Department of Clinical Medicine, Anaesthesiology and Intensive Care, Lund University;

关键词: Machine learning;    Artificial intelligence;    Artificial neural networks;    Intensive care;    Critical care;    Mortality;   

DOI  :  10.1186/s40560-019-0393-1

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown