Early Prediction of Sepsis Onset Using Neural Architecture Search Based on Genetic Algorithms

Jae Kwan Kim, Wonbin Ahn, Sangin Park, Soo-Hong Lee and Laehyun Kim
Additional contact information
Jae Kwan Kim: Center for Bionics, Korea Institute of Science and Technology, Seoul 02792, Korea
Wonbin Ahn: Applied AI Research Lab, LG AI Research, Seoul 07796, Korea
Sangin Park: Center for Bionics, Korea Institute of Science and Technology, Seoul 02792, Korea
Soo-Hong Lee: School of Mechanical Engineering, Yonsei University, Seoul 03722, Korea
Laehyun Kim: Center for Bionics, Korea Institute of Science and Technology, Seoul 02792, Korea

IJERPH, 2022, vol. 19, issue 4, 1-16

Abstract: Sepsis is a life-threatening condition with a high mortality rate. Early prediction and treatment are the most effective strategies for increasing survival rates. This paper proposes a neural architecture search (NAS) model to predict the onset of sepsis with a low computational cost and high search performance by applying a genetic algorithm (GA). The proposed model shares the weights of all possible connection nodes internally within the neural network. Externally, the search cost is reduced through the weight-sharing effect between the genotypes of the GA. A predictive analysis was performed using the Medical Information Mart for Intensive Care III (MIMIC-III), a medical time-series dataset, with the primary objective of predicting sepsis onset 3 h before occurrence. In addition, experiments were conducted under various prediction times (0–12 h) for comparison. The proposed model exhibited an area under the receiver operating characteristic curve (AUROC) score of 0.94 (95% CI: 0.92–0.96) for 3 h, which is 0.31–0.26 higher than the scores obtained using the Sequential Organ Failure Assessment (SOFA), quick SOFA (qSOFA), and Simplified Acute Physiology Score (SAPS) II scoring systems. Furthermore, the proposed model exhibited a 12% improvement in the AUROC value over a simple model based on the long short-term memory neural network. Additionally, it is not only optimally searchable for sepsis onset prediction, but also outperforms conventional models that use similar predictive purposes and datasets. Notably, it is sufficiently robust to shape changes in the input data and has less structural dependence.

Keywords: genetic algorithm; intensive care unit; neural architecture search; sepsis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/4/2349/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/4/2349/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:4:p:2349-:d:752473

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().