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

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  • Jae Kwan Kim

    (Center for Bionics, Korea Institute of Science and Technology, Seoul 02792, Korea
    School of Mechanical Engineering, Yonsei University, Seoul 03722, 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
    Department of HY-KIST Bio-Convergence, Hanyang University, Seoul 04763, Korea)

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.

Suggested Citation

  • Jae Kwan Kim & Wonbin Ahn & Sangin Park & Soo-Hong Lee & Laehyun Kim, 2022. "Early Prediction of Sepsis Onset Using Neural Architecture Search Based on Genetic Algorithms," IJERPH, MDPI, vol. 19(4), pages 1-16, February.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:4:p:2349-:d:752473
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    References listed on IDEAS

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    1. Peng Liu & Peijun Zheng & Ziyu Chen, 2019. "Deep Learning with Stacked Denoising Auto-Encoder for Short-Term Electric Load Forecasting," Energies, MDPI, vol. 12(12), pages 1-15, June.
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