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一个机器学习模型用于预测在学龄儿童中严重的mycoplasma pneumoniae肺炎

Yingying Ye¹, Zhenpeng Gao¹, Zhiling Zhang¹

¹Department of Infectious Diseases, Children's Hospital of Soochow University, No. 303, Jingde Road, Suzhou, China.

BMC infectious diseases

|April 21, 2025

概括

一个可解释的CatBoost机器学习模型准确地预测了儿童严重的Mycoplasma pneumoniae肺炎. 关键预测因素包括发烧持续时间,D-模和炎症标志物,有助于早期诊断和治疗.

科学领域:

儿童传染病儿童传染病
计算医学是一种计算医学.
医疗保健中的机器学习

背景情况:

菌性肺炎肺炎 (MPP) 是一个常见的儿童呼吸道感染.
早期发现严重病例 (SMPP) 对于有效管理和预防并发症至关重要.
当前的诊断方法在预测疾病严重程度时可能缺乏准确性.

研究的目的:

开发一种可解释的机器学习 (ML) 模型,用于预测学龄儿童的严重Mycoplasma pneumoniae肺炎 (SMPP).
确定可靠的临床预测因子来分类MPP的严重程度.
通过可解释的AI方法来增强临床决策.

主要方法:

收集了483名患有MPP的儿童的临床数据.
使用差异分析和逻辑回归来进行特征选择.
开发并验证了8个ML算法,包括CatBoost,使用AUC,准确性和决策曲线分析 (DCA).
使用Shapley添加式解释 (SHAP) 进行模型解释和特征重要性排名.

主要成果:

CatBoost模型表现出卓越的性能,AUC为0.934和准确度为0.9175.
决策曲线分析证实了CatBoost模型对其他ML模型的临床实用性.
SHAP分析确定了关键预测因素:发烧持续时间,D-二次体,血小板计数 (PLT),C反应蛋白 (CRP),乳酸脱酶 (LDH) 和中性粒细胞与淋巴细胞比率 (NLR).

结论:

一个可解释的CatBoost模型准确地识别了患有SMPP的儿童.
早期发现有助于优化治疗和预防并发症.
SHAP提高了模型的透明度,增加了对临床应用的信任.

关键词:

在 CatBoost 中使用 CatBoost.机器学习机器学习菌性肺炎 (Mycoplasma pneumoniae) 是一种肺炎.学校年龄的孩子们.

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