Artificial neural networks improve and simplify intensive care mortality prognostication: a national cohort study of 217,289 first-time intensive care unit admissions
(2019) In Journal of Intensive Care 7.- 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... (More) - 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 textless10-15 for AUC and p textless10-5 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 textless10-5). 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/486bda86-9c1e-4a44-ac83-b55528f96d7a
- author
- Holmgren, Gustav LU ; Andersson, Peder LU ; Jakobsson, Andreas LU and Frigyesi, Attila LU
- organization
- publishing date
- 2019
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- artificial intelligence, artificial neural networks, critical care, intensive care, machine learning, mortality, prediction, survival
- in
- Journal of Intensive Care
- volume
- 7
- article number
- 44
- pages
- 8 pages
- publisher
- BioMed Central (BMC)
- external identifiers
-
- scopus:85070980594
- pmid:31428430
- ISSN
- 2052-0492
- DOI
- 10.1186/s40560-019-0393-1
- language
- English
- LU publication?
- yes
- id
- 486bda86-9c1e-4a44-ac83-b55528f96d7a
- date added to LUP
- 2019-09-10 10:43:49
- date last changed
- 2022-04-26 05:11:44
@article{486bda86-9c1e-4a44-ac83-b55528f96d7a, abstract = {{Purpose <br/>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. <br/>Results <br/>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 textless10-15 for AUC and p textless10-5 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 textless10-5). Furthermore, the ANN model was superior in correcting mortality for age. <br/>Conclusion <br/>ANNs can outperform the SAPS 3 model for early prediction of 30-day mortality for intensive care patients.}}, author = {{Holmgren, Gustav and Andersson, Peder and Jakobsson, Andreas and Frigyesi, Attila}}, issn = {{2052-0492}}, keywords = {{artificial intelligence; artificial neural networks; critical care; intensive care; machine learning; mortality; prediction; survival}}, language = {{eng}}, publisher = {{BioMed Central (BMC)}}, series = {{Journal of Intensive Care}}, title = {{Artificial neural networks improve and simplify intensive care mortality prognostication: a national cohort study of 217,289 first-time intensive care unit admissions}}, url = {{http://dx.doi.org/10.1186/s40560-019-0393-1}}, doi = {{10.1186/s40560-019-0393-1}}, volume = {{7}}, year = {{2019}}, }