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关于机器学习和人工智能在新生儿坏死性小肠结肠炎研究中的现状综述

State of the art review on machine learning and artificial intelligence in the study of neonatal necrotizing enterocolitis.

作者信息

McElroy Steven J, Lueschow Shiloh R

机构信息

Department of Pediatrics, University of California Davis, Sacramento, CA, United States.

Stead Family Department of Pediatrics, University of Iowa, Iowa City, IA, United States.

出版信息

Front Pediatr. 2023 May 26;11:1182597. doi: 10.3389/fped.2023.1182597. eCollection 2023.

Abstract

Necrotizing Enterocolitis (NEC) is one of the leading causes of gastrointestinal emergency in preterm infants. Although NEC was formally described in the 1960's, there is still difficulty in diagnosis and ultimately treatment for NEC due in part to the multifactorial nature of the disease. Artificial intelligence (AI) and machine learning (ML) techniques have been applied by healthcare researchers over the past 30 years to better understand various diseases. Specifically, NEC researchers have used AI and ML to predict NEC diagnosis, NEC prognosis, discover biomarkers, and evaluate treatment strategies. In this review, we discuss AI and ML techniques, the current literature that has applied AI and ML to NEC, and some of the limitations in the field.

摘要

坏死性小肠结肠炎(NEC)是早产儿胃肠道急症的主要原因之一。尽管NEC在20世纪60年代就有了正式描述,但由于该疾病的多因素性质,NEC的诊断乃至治疗仍存在困难。在过去30年里,医疗保健研究人员应用人工智能(AI)和机器学习(ML)技术来更好地了解各种疾病。具体而言,NEC研究人员已使用AI和ML来预测NEC诊断、NEC预后、发现生物标志物以及评估治疗策略。在本综述中,我们讨论了AI和ML技术、已将AI和ML应用于NEC的当前文献以及该领域的一些局限性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/feab/10250644/76b9f6231c8d/fped-11-1182597-g001.jpg

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