Machine-learning based prediction of appendicitis for patients presenting with acute abdominal pain at the emergency department.

ABSTRACT:

Background

Acute abdominal pain (AAP) constitutes 5-10% of all emergency department (ED) visits, with appendicitis being a prevalent AAP etiology often necessitating surgical intervention. The variability in AAP symptoms and causes, combined with the challenge of identifying appendicitis, complicate timely intervention. To estimate the risk of appendicitis, scoring systems such as the Alvarado score have been developed. However, diagnostic errors and delays remain common. Although various machine learning (ML) models have been proposed to enhance appendicitis detection, none have been seamlessly integrated into the ED workflows for AAP or are specifically designed to diagnose appendicitis as early as possible within the clinical decision-making process. To mimic daily clinical prac

SUBMITTER: Schipper A 

PROVIDER: S-EPMC11664873 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature