使用多种机器学习算法开发新生儿医院获得的胃肠道感染预测模型
Hui Shao1, Huajuan Chen2, Xiujuan Wang1
1Department of Infectology, Shaoxing Maternity and Child Health Care Hospital (Maternity and Child Health Care Affiliated Hospital, Shaoxing University), Shaoxing, People's Republic of China.
Infection and drug resistance
|August 27, 2025
概括
一个使用机器学习的新预测模型准确地识别出患有胃肠道感染的新生儿,从而实现早期临床干预和改善结果.
科学领域:
- 新生儿医学
- 计算生物学
- 传染性疾病
背景情况:
- 新生儿胃肠道感染在重症监护机构中存在重大风险.
- 早期识别和干预对于改善新生儿结果至关重要.
研究的目的:
- 开发和验证用于预测新生儿胃肠道感染的机器学习模型.
- 确定与新生儿这些感染相关的关键风险因素.
主要方法:
- 对176名胃肠道感染的新生儿和675名对照的回顾性分析.
- 使用机器学习算法,包括神经网络,具有特征选择方法 (Boruta,Lasso).
- 应用SMOTE用于类不平衡和SHAP用于模型解释性.
主要成果:
- 一个神经网络模型确定了九个重要的风险因素,包括妊娠年龄和中央静脉导管.
- 该模型在训练 (AUC=0.895) 和测试 (AUC=0.876) 组中都实现了高性能指标.
- 为临床使用开发了一个互动网络计算器.
结论:
- 经过验证的预测模型能够早期识别患有胃肠道感染的新生儿.
- 支持临床决策及时干预,可能减少与感染相关的发病率.
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