Predictive Analytics for Early Detection of Sepsis Using Deep Learning Models in Healthcare

Abstract

In 2020, the emergence of deep learning models has significantly enhanced predictive analytics in healthcare. This study explores the development of a deep-learning framework for the early detection of sepsis in hospitalized patients. The model was trained on electronic health records (EHRs) from multiple hospitals, using vital signs, lab results, and demographic data. By leveraging recurrent neural networks (RNNs) with attention mechanisms, the model demonstrated superior performance in predicting sepsis onset up to 24 hours before clinical diagnosis. This early detection capability has the potential to reduce mortality rates and improve patient outcomes through timely intervention.