儿童慢性病早期诊断的症状 - 一个基于机器学习的得分
Paulo Cesar Koch Nogueira1,2, Auberth Henrik Venson3, Maria Fernanda Camargo de Carvalho4
1Pediatrics Department, UNIFESP - Escola Paulista de Medicina, Rua Guapiaçu 121 ap 91, 04024-020, Vila Clementino, Sao Paulo, Brazil. pckoch@uol.com.br.
European journal of pediatrics
|May 26, 2023
概括
儿童慢性病 (CKD) 的早期检测至关重要. 这项研究使用机器学习确定了12种易于验证的症状,以帮助儿童早期诊断CKD,特别是在初级保健中.
科学领域:
- 儿科脏病学 儿科脏病学
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 儿科慢性病 (CKD) 的晚期诊断导致患病率增加.
- 在儿童中对全人口进行CKD查是不具有成本效益的.
研究的目的:
- 确定关键的迹象和症状来分类患有CKD风险的儿科患者.
- 利用机器学习开发用于儿童早期CKD检测的预测模型.
主要方法:
- 一项病例控制研究,涉及376名患有CKD的儿童和376名健康对照.
- 利用决策树和极端梯度提升 (XGBoost) 模型来分析问卷数据.
- 使用曲线下的接收器操作特征面积 (ROC AUC) 评估模型性能.
主要成果:
- 在XGBoost模型中,确定了12个变量,区分CKD患者与健康儿童.
- 决策树模型确定了与CKD相关的六个变量.
- 与决策树模型 (ROC AUC = 0.896) 相比,XGBoost模型的准确性更高 (ROC AUC = 0.939).
结论:
- 已经出现了12种容易验证的症状,作为儿科慢性病的显著风险指标.
- 这些发现可以提高诊断意识,特别是在初级保健机构.
- 早期识别有风险的儿童可以提高诊断效率,减少治疗延迟.
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