基于机器学习的预测手术干预的早产婴儿与死结肠炎:一个回顾性队列研究
Ying Li1, Peipei Zhang2, Jing Wu3
1Capital Institute of Pediatrics, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China.
Children (Basel, Switzerland)
|January 28, 2026
概括
机器学习模型准确地预测了患有死角性肠球炎 (NEC) 的早产婴儿的手术需求. 这有助于早期识别和手术管理,以获得新生儿更好的结果.
科学领域:
- 新生儿医学 新生儿医学
- 计算生物学 计算生物学
- 儿科手术 儿科手术
背景情况:
- 结核性肠球炎 (NEC) 是新生儿,特别是早产婴儿的关键性胃肠道疾病.
- 由于模糊的症状和快速进展,早期识别需要手术的NEC是困难的.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测早产NEC的手术干预.
- 确定NEC中手术风险评估的关键临床,实验室和成像变量.
主要方法:
- 对320名早产婴儿进行了回顾性队列研究,这些婴儿患有NEC.
- 在43个变量上使用LASSO回归和Boruta算法进行特征选择.
- 开发和评估LightGBM,XGBoost,随机森林和神经网络模型.
主要成果:
- 选择了13个关键特征,包括妊娠年龄,CRP和腹征兆.
- 神经网络显示了最高的准确性 (0.875),随机森林最高的ROC-AUC (0.922),XGBoost最高的PR-AUC (0.867).
- SHAP分析强调了CRP,腹征兆和妊娠年龄作为关键预测因素.
结论:
- 集成的ML模型有效地预测了早产NEC的外科干预需求.
- 这些模型为早期诊断和优化外科管理提供客观的决策支持.
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