医学
机器学习
决策树
坏死性小肠结肠炎
人工智能
队列
儿科
病理
计算机科学
作者
Shiloh R. Lueschow,Timothy J. Boly,Elizabeth A. Jasper,Ravi Patel,Steven J. McElroy
标识
DOI:10.1038/s41390-021-01570-y
摘要
Necrotizing enterocolitis (NEC) is a devastating intestinal disease of premature infants, with significant mortality and long-term morbidity among survivors. Multiple NEC definitions exist, but no formal head-to-head evaluation has been performed. We hypothesized that contemporary definitions would perform better in evaluation metrics than Bell’s and range features would be more frequently identified as important than yes/no features. Two hundred and nineteen patients from the University of Iowa hospital with NEC, intestinal perforation, or NEC concern were identified from a 10-year retrospective cohort. NEC presence was confirmed by a blinded investigator. Evaluation metrics were calculated using statistics and six supervised machine learning classifiers for current NEC definitions. Feature importance evaluation was performed on each decision tree classifier. Newer definitions outperformed Bell’s staging using both standard statistics and most machine learning classifiers. The decision tree classifier had the highest overall machine learning scores, which resulted in Non-Bell definitions having high sensitivity (0.826, INC) and specificity (0.969, ST), while Modified Bell (IIA+) had reasonable sensitivity (0.783), but poor specificity (0.531). Feature importance evaluation identified nine criteria as important for diagnosis. This preliminary study suggests that Non-Bell NEC definitions may be better at diagnosing NEC and calls for further examination of definitions and important criteria.
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