机器学习在新生儿坏死性小肠结肠炎诊疗中的研究进展

Recent research on machine learning in the diagnosis and treatment of necrotizing enterocolitis in neonates

摘要:

新生儿坏死性小肠结肠炎（neonatal necrotizing enterocolitis，NEC）以血便、腹胀、呕吐等为主要表现，是导致新生儿死亡的主要原因之一，早期识别和诊断对该病预后极为重要。机器学习的兴起和发展为早期、快速、准确识别该病提供了可能。该文总结近年来机器学习在NEC应用中的算法，分析算法揭示的高危预测因子，评价机器学习在NEC病因回溯、定义、诊断方面的能力和特点，探讨机器学习在NEC未来应用中的挑战及前景。

Abstract:

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.