Recent research on machine learning in the diagnosis and treatment of necrotizing enterocolitis in neonates.
10.7499/j.issn.1008-8830.2302165
- Author:
Cheng CUI
1
;
Fei-Long CHEN
1
;
Lu-Quan LI
1
Author Information
1. Department of Neonatology, Children's Hospital of Chongqing Medical University/National Clinical Research Center for Child Health and Disorders/Ministry of Education Key Laboratory of Child Development and Disorders/Key Laboratory of Pediatrics in Chongqing, Chongqing 400014, China.
- Publication Type:Journal Article
- Keywords:
Auxiliary diagnosis;
Classification algorithms;
Machine learning;
Necrotizing enterocolitis;
Neonate
- MeSH:
Infant, Newborn;
Humans;
Enterocolitis, Necrotizing/therapy*;
Infant, Newborn, Diseases;
Prognosis;
Gastrointestinal Hemorrhage/diagnosis*;
Machine Learning
- From:
Chinese Journal of Contemporary Pediatrics
2023;25(7):767-773
- CountryChina
- Language:Chinese
-
Abstract:
Necrotizing enterocolitis (NEC), with the main manifestations of bloody stool, abdominal distension, and vomiting, is one of the leading causes of death in neonates, and early identification and diagnosis are crucial for the prognosis of NEC. The emergence and development of machine learning has provided the potential for early, rapid, and accurate identification of this disease. This article summarizes the algorithms of machine learning recently used in NEC, analyzes the high-risk predictive factors revealed by these algorithms, evaluates the ability and characteristics of machine learning in the etiology, definition, and diagnosis of NEC, and discusses the challenges and prospects for the future application of machine learning in NEC.
