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糖尿病病的风险分层:对方法进步和临床应用的预测模型的审查

Yazhi Wang¹, Jianzhou Wang², Hui Chen³

¹The Second School of Clinical Medicine, Lanzhou University, Lanzhou, Gansu, 730000, China.

Journal of translational medicine

|February 5, 2026

概括

糖尿病病 (DKD) 的准确预测模型对于早期干预至关重要. 本综述探讨了机器学习和多式联络数据,以改善DKD的诊断和预后,提高患者的护理.

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
生物统计学生物统计学

背景情况:

糖尿病病 (DKD) 是全球末期病 (ESRD) 的主要原因之一.
DKD表现出复杂的病理生理学和不同的进展率.
早期预测DKD的发病,进展和结果对于及时干预和量身定制的患者管理至关重要.

研究的目的:

系统地审查目前关于DKD的诊断和预后预测模型的研究.
检查开发和验证DKD预测模型的方法考虑,包括机器学习应用.
用临床参数,多组学,成像,视网膜和病理数据评估预测模型,并确定临床翻译的挑战和未来方向.

主要方法:

在DKD中对预测模型的系统文献综述.
专注于机器学习 (ML) 技术,用于模型开发和验证.
基于常规临床数据和多模式数据集成 (多omics,成像,视网膜,病理学) 的模型评估.

主要成果:

总结了关于DKD预测模型的当前研究,涵盖诊断和预后应用.
突出了ML在构建复杂预测工具中的作用.
评估使用各种数据源的模型的性能,从临床参数到先进的多omics和成像数据.

结论:

优化和翻译DKD预测模型可以显著提高患者护理.
未来的研究应该解决当前的挑战,以推进风险分层和个性化的DKD管理.
改进的预测模型将为脏病学中的精准医学策略提供信息.

关键词:

生物标志物生物标志物糖尿病脏疾病糖尿病脏疾病机器学习是机器学习.多式联运数据多式联运数据预测模型的预测模型.风险评估风险评估风险评估风险分层是指风险的分层.

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