33,579个尿路结石的临床管理影响:构成的新型模式,并发症,季节性变化和基于机器学习的尿路结石预测
Zhenglin Chang1,2,3, Xiujing Han1, Han Zhao2
1Department of Clinical Laboratory, State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
International journal of surgery (London, England)
|August 11, 2025
概括
这项大规模的研究揭示了中国南部的各种尿路结石成分和危险因素. 一种新的机器学习模型可以预测尿液,从而改善了尿病患者的风险分层.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 腎臟病學 (nephrology) 是一種醫學專業.
- 数据科学数据科学数据科学
背景情况:
- 尿病影响着全球大量的人口,但关于石头组成,并发病症和感染风险的全面数据是有限的.
- 了解这些模式对于有效的患者管理和预防策略至关重要.
研究的目的:
- 分析尿路结石的组成模式与伴随疾病,季节性和中国南部的感染风险有关.
- 开发一种机器学习模型,用于预测尿病患者的尿症风险.
主要方法:
- 分析了来自中国南部的33579颗尿路结石 (2014-2024年).
- 对石材成分的检查与人口统计学,并发病症和季节性/感染因素进行对比.
- 机器学习模型的开发,用于预测尿.
主要成果:
- 石占主导地位,其次是感染和尿酸石头.
- 在石头类型和相关的并发症中观察到显著的性别差异.
- 在石头形成和风险因素中确定了明显的解剖学,季节性和与年龄相关的模式.
- 开发了一个强大的机器学习模型,用于预测尿,整合了关键的临床参数.
结论:
- 这项研究提供了亚洲尿路石组成的最广泛的分析,突出了新的关联模式.
- 开发的机器学习模型在 urolithiasis 的临床风险分层方面取得了重大进展.
- 这些发现强调了在尿病管理中考虑多种因素的重要性.
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