机器学习在新生儿坏死性小肠结肠炎诊疗中的研究进展

doi:10.7499/j.issn.1008-8830.2302165

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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.

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	Articles by authors
	CUI Cheng
	CHEN Fei-Long
	LI Lu-Quan

Key words： Necrotizing enterocolitis Machine learning Classification algorithms Auxiliary diagnosis Neonate

Received: 27 February 2023

Cite this article:

CUI Cheng,CHEN Fei-Long,LI Lu-Quan. Recent research on machine learning in the diagnosis and treatment of necrotizing enterocolitis in neonates[J]. CJCP, 2023, 25(7): 767-773.

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http://www.zgddek.com/EN/10.7499/j.issn.1008-8830.2302165 OR http://www.zgddek.com/EN/Y2023/V25/I7/767

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