[新生儿坏死性小肠结肠炎诊断与治疗中机器学习的最新研究]
[Recent research on machine learning in the diagnosis and treatment of necrotizing enterocolitis in neonates].
作者信息
Cui Cheng, Chen Fei-Long, Li Lu-Quan
机构信息
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.
出版信息
Zhongguo Dang Dai Er Ke Za Zhi. 2023 Jul 15;25(7):767-773. doi: 10.7499/j.issn.1008-8830.2302165.
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.
坏死性小肠结肠炎（NEC）主要表现为血便、腹胀和呕吐，是新生儿死亡的主要原因之一，早期识别和诊断对NEC的预后至关重要。机器学习的出现和发展为早期、快速、准确地识别这种疾病提供了可能。本文总结了近期用于NEC的机器学习算法，分析了这些算法所揭示的高危预测因素，评估了机器学习在NEC病因、定义及诊断方面的能力和特点，并探讨了机器学习在NEC未来应用中的挑战与前景。
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