Feature augmentation and semi-supervised conditional transfer learning for early detection of sepsis

Abstract

Early detection of Sepsis is crucial for improving patient outcomes, as it is a significant public health concern that results in substantial morbidity and mortality. However, despite the widespread use of the Sequential Organ Failure Assessment (SOFA) in clinical settings to identify sepsis, obtaining sufficient physiological data before onset remains challenging, limiting early detection of sepsis. To address this challenge, we propose an interpretable machine learning model, ITFG (Interpretable Tree-based Feature Generation), that leverages potential correlations between features based on existing knowledge to identify sepsis within six hours of onset using valuable and continuous physiological measures. Furthermore, we introduce a Semi-supervised Attention-based Conditional Transfer Learning (SAC-TL) framework to enhance the model's generality and enable it to be used for early warning of sepsis in the target domain with less information from the source domain. Our proposed approaches effectively address the problem of systematic feature sparsity and missing data, while also being practical for different degrees of generalizability. We evaluated our proposed approaches on open datasets, MIMIC and PhysioNet, obtaining AUC of 97.98% and 86.21%, respectively, demonstrating their effectiveness in different data environments and achieving the best early detection results.

Keywords: Feature augmentation; Sepsis early detection; Transfer learning.