机器学习模型用于查超重和肥胖人口中临床显著的结石病
Hao-Wei Chen1,2,3, Jung-Ting Lee4, Pei-Siou Wei5
1Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, 100, Shih-Chuan 1st Road, Kaohsiung, Taiwan.
World journal of urology
|March 9, 2024
概括
机器学习模型使用简单的临床和尿液数据有效地查超重和肥胖个体的结石. 这种工具有助于在风险人群中早期发现和干预结石病.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 超重和肥胖是结石 (结石) 的危险因素.
- 早期发现临床意义上的结石病对于预防并发症至关重要.
- 目前的查方法可能对这些人群来说不是最佳的.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于查临床显著的结石病.
- 为查模型使用易于获得的临床和尿液参数.
- 针对超重和肥胖的人群 (BMI ≥25 kg/m2).
主要方法:
- 开发了使用参数的ML模型:性别,年龄,BMI,痛风,糖尿病,估计的膜过率,细菌尿,尿液pH,尿液红细胞计数和尿液特异性重量.
- 从台湾高雄市的2928名受试者 (2012-2021) 收集的数据.
- 在574名受试者的测试数据集上验证了模型.
主要成果:
- 该研究包括2928名受试者;39.21%的人患有临床显著的结石病.
- 最好的ML模型在测试数据集上实现了0.965的曲线下的面积.
- 高性能指标:灵敏度为0.860,特异性为0.947,正预测值为0.918,负预测值为0.907.
结论:
- 基于ML的模型有效地区分了患有临床显著结石病的超重/肥胖个体.
- 这个模型可以作为一个可访问和可靠的查工具.
- 促进早期干预,改变生活方式和服用药物来预防石头并发症.
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