The Artificial Neural Network as a Diagnostic Tool of the Risk of Clostridioides difficile Infection among Patients with Chronic Kidney Disease-Reference-Cited by-同舟云学术

The Artificial Neural Network as a Diagnostic Tool of the Risk of Clostridioides difficile Infection among Patients with Chronic Kidney Disease

Published:2023-07-18 Issue:14 Volume:12 Page:4751
ISSN:2077-0383
Container-title:Journal of Clinical Medicine
language:en
Short-container-title:JCM

Author:

Stojanowski Jakub¹^ORCID,Konieczny Andrzej¹^ORCID,Lis Łukasz²^ORCID,Frosztęga Weronika¹,Brzozowska Patrycja¹,Ciszewska Anna²,Rydzyńska Klaudia¹^ORCID,Sroka Michał²,Krakowska Kornelia¹,Gołębiowski Tomasz¹^ORCID,Hruby Zbigniew²,Kusztal Mariusz¹^ORCID,Krajewska Magdalena¹^ORCID

Affiliation:

1. Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, Borowska 213, 50-556 Wroclaw, Poland

2. Department of Nephrology with Transplantation and Internal Medicine Subunits, Regional Specialistic Hospital, Kamienskiego 73a, 51-124 Wroclaw, Poland

Abstract

The majority of recently published studies indicate a greater incidence and mortality due to Clostridioides difficile infection (CDI) in patients with chronic kidney disease (CKD). Hospitalization, older age, the use of antibiotics, immunosuppression, proton pump inhibitors (PPI), and chronic diseases such as CKD are responsible for the increased prevalence of infections. The aim of the study is to identify clinical indicators allowing, in combination with artificial intelligence (AI) techniques, the most accurate assessment of the patients being at elevated risk of CDI.

Funder

Wroclaw Medical University

Publisher

MDPI AG

Subject

General Medicine

Link

https://www.mdpi.com/2077-0383/12/14/4751/pdf

Reference17 articles.

1. Clinical Practice Guidelines for Clostridium difficile Infection in Adults and Children: 2017 Update by the Infectious Diseases Society of America (IDSA) and Society for Healthcare Epidemiology of America (SHEA);McDonald;Clin. Infect. Dis.,2018

2. Chicco, D., and Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom., 21.

3. Lis, Ł., Konieczny, A., Sroka, M., Ciszewska, A., Krakowska, K., Gołębiowski, T., and Hruby, Z. (2022). Clinical Determinants Predicting Clostridioides difficile Infection among Patients with Chronic Kidney Disease. Antibiotics, 11.

4. Clostridium difficile Infection Risk with Important Antibiotic Classes: An Analysis of the FDA Adverse Event Reporting System;Teng;Int. J. Med. Sci.,2019

5. Clostridium difficile Infection among US Emergency Department Patients with Diarrhea and No Vomiting;Abrahamian;Ann. Emerg. Med.,2017

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Prediction of 28-Day All-Cause Mortality in Heart Failure Patients with Clostridioides difficile Infection Using Machine Learning Models: Evidence from the MIMIC-IV Database;Cardiology;2024-08-17