Early Prediction of Sepsis Utilizing Self-Regulated Generative Adversarial Network and Deep Transfer Fuzzy SVM

Sepsis is a life-threatening condition that can be fatal. Advances in big data analytics and the data-rich environment of intensive care units have enabled the development of artificial intelligence-based early warning systems that offer a solution to reduce sepsis-related mortality. Support Vector Machines (SVMs) establish optimal classifiers and deep learning automatically learns key features. This paper proposes an early warning system for sepsis that combines a SVM and deep learning. A hybrid encoder model that integrates a convolutional neural network (CNN), a bi-directional long short-term memory (BiLSTM), an attention mechanism (AM) and a fully connected network (FCN) are used to extract critical features. To improve feature robustness, a two-channel self-regulated generative adversarial network is used: the cooperative channel learns predictive features and the adversarial channel prevents the acquisition of erroneous features. The refined features are then input into a novel deep transfer least squares fuzzy hyperplane-based support vector machine (DT-LS FH SVM) that uses statistical learning theory, fuzzy theory, and transfer learning to construct an optimal sepsis warning system. This warning system predicts sepsis-related adverse outcomes up to 28 days in advance to give early warning and mitigate the risk of septic-related mortality. The proposed method demonstrates satisfactory predictive performance, with an area under the receiver operating characteristic curve (AUROC) of 0.793589, highlighting the feasibility of leveraging early integrated data to predict adverse outcomes within 28 days among patients with sepsis. The experimental results show that the proposed model outperforms current state-of-the-art methods.