Early Prediction of Sepsis Using Time Series Forecasting

• Main Authors: Xu, Jinghua, Minakova, Natalia, Sanchez, Pablo Ortega, Riezler, Stefan
• Format: Conference Proceeding
• Language: English
• Subjects: Biological system modeling; deep learning; Physiology; Predictive models; Receivers; Sepsis; Time series analysis; transformer; Transformers
• ISSN: 2325-3703
• DOI: 10.1109/e-Science58273.2023.10254852

Sepsis is a serious complication of an infection. Without quick treatment it can lead to organ failure and death. Early detection and treatment of sepsis can thus improve patient outcomes. Yet, their effectiveness often relies on awareness and acceptance of said procedures. In this work, we implement sepsis check based on a widely accepted guideline for sepsis recognition (Sepsis-3). Our implementation achieved F-score as high as 0.874. In addition to implementing the ruled-based approach to early sepsis detection, we use an existing data-driven transformer-based STraTS model [1] for time-series forecasting to support sepsis check and directly predicting sepsis label using 24-hour patient data in a fully data-driven setup. The advantage of time series forecasting is improved handling of missing data and the potential of applying the Sepsis-3 definition to unobserved forecast data. Additionally, we attempt to improve STraTS model by integrating a clinical text embedding module to enable multimodal learning. Both the original STraTS model and our refined STraTS+Text model perform good in both forecasting (masked MSE, mean squared error at approximately 5.24) and classification task (ROC-AUC, area under receiver operating characteristic curve at approximately 0.89).