Evaluating the impact of common clinical confounders on performance of deep-learning-based sepsis risk assessment.

ABSTRACT:

Introduction

Early identification of sepsis in the emergency department using machine learning remains a challenging problem, primarily due to the lack of a gold standard for sepsis diagnosis, the heterogeneity in clinical presentations, and the impact of confounding conditions.

Methods

In this work, we present a deep-learning-based predictive model designed to enable early detection of patients at risk of developing sepsis, using data from the first 24 h of admission. The model is based on routine blood test results commonly performed on patients, including CBC (Complete Blood Count), CMP (Comprehensive Metabolic Panel), lipid panels, vital signs, age, and sex. To address the challenge of label uncertainty as a part of the training process, we explore two different definiti

SUBMITTER: Chaganti S 

PROVIDER: S-EPMC12305701 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature