Efficient sepsis detection using deep learning and residual convolutional networks.

ABSTRACT: Sepsis is a life-threatening complication caused by infection that leads to extensive tissue damage. If not treated promptly, it can become fatal. Early identification and diagnosis of sepsis are critical to improving patient outcomes. Although recent technological advancements have aided sepsis detection, challenges remain in timely diagnosis using standard clinical practices. In this article, we present a new deep learning model to detect the occurrence of sepsis and the African vulture optimization algorithm (AVOA) to enhance the model performance. The system comprises four crucial steps: First, the enhanced convolutional learning framework (ECLF) with atrous convolutional and multi-level strategies that aim to learn high-level features from the nonlinear mapping of the medical data. Se

SUBMITTER: S Almasoud A 

PROVIDER: S-EPMC12453647 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature