Sepsis Detection

Sepsis detection research focuses on developing accurate and timely prediction models to improve patient outcomes, primarily leveraging machine learning algorithms. Current efforts concentrate on utilizing diverse data sources, including heart rate variability, wearable sensor data (e.g., PPG, IMU), and electronic health records, with models ranging from simple ensemble methods (like XGBoost and Random Forest) to more complex neural networks. These advancements aim to enable earlier sepsis identification, potentially through real-time alerts from wearable devices or improved analysis of existing clinical data, ultimately leading to faster treatment and reduced mortality. Furthermore, research addresses challenges like data privacy and model interpretability to facilitate wider clinical adoption.