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

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.

Real-Time Prediction of Sepsis in Critical Trauma Patients: Machine Learning–Based Modeling Study

“…Nemati et al [ 35 ] achieved an AUROC of 0.85 with a modified Weibull-Cox proportional hazards model for predicting sepsis 6 hours in advance, and Yang et al [ 28 ] achieved similar performance, also with the XGBoost algorithm. Kim et al [ 36 ] recently developed a type of deep learning model to predict sepsis that had higher discrimination performance than our model, with an AUROC of 0.91. However, their model could be seen as a complex black box due to its lack of interpretability, which might limit its acceptance among clinicians.…”

“…However, model calibration decreased as the prediction window shortened, which might be associated with a decreasing number of positive steps due to the reduction of the prediction window. Several studies have reported a similar trend for AUROC across different prediction windows but have not reported changes in AUPRC or calibration [ 35 , 36 ]. Most importantly, a model with good discrimination and calibration performance does not necessarily have high clinical value [ 42 ].…”

Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes

“…However, most ML frameworks require determining the time of sepsis onset ( T 0 ) or the initiation of a period when patterns are consistent with sepsis as a distinct entity. Kim et al (33) recently adopted this approach. Larie et al (34) implemented ANNs using long short-term memory (LSTM) and multilayer perceptrons for sepsis prediction.…”

“…Deep learning algorithms have also shown promise in analyzing medical time series data despite the challenge of dealing with sensor- and noise-based errors (33). However, small sample sizes can lead to overfitting, which can be addressed using self-supervised learning, transfer learning, or data augmentation (49).…”

“…However, small sample sizes can lead to overfitting, which can be addressed using self-supervised learning, transfer learning, or data augmentation (49). Kim et al (33) applied a RNN to a time series dataset using a neural architecture search method to optimize the architecture and a genetic algorithm approach to balance the computational resources and search efficiency. An autoencoder was also used to denoise the data and improve the learning process.…”