人工智能预测慢性病进展到衰竭:叙述性审查
Zane A Miller1,2, Karen Dwyer1,3
1Faculty of Medicine, Dentistry & Health Sciences Melbourne, The University of Melbourne, Melbourne, Victoria, Australia.
Nephrology (Carlton, Vic.)
|January 7, 2025
概括
机器学习模型可以使用功能和蛋白尿等关键变量预测慢性病进展到衰竭. 这些人工智能模型显示出有希望的结果,通常表现优于功能衰竭风险方程.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 慢性病 (CKD) 涉及逐渐丧失功能,使衰竭的预测具有挑战性.
- 人工智能 (AI),特别是机器学习 (ML),为改善CKD进展预测提供了潜力.
研究的目的:
- 审查ML模型中用于预测渐进性CKD的常见和重要变量.
- 将ML模型的性能与已建立的功能衰竭风险方程 (KFRE) 进行比较.
主要方法:
- 发表在Ovid Medline和EMBASE杂志上的研究的叙事综述.
- 关键词包括"慢性病"",机器学习"和"末期病".
- 数据提取集中在ML模型中使用的变量上;包括16项研究.
主要成果:
- 随机森林,支持向量机器和XGBoost是常见的ML模型.
- 关键变量包括年龄,性别,功能,蛋白尿和全血检查.
- ML模型优于或不低于KFRE.
结论:
- 确定了关键变量,用于开发准确的ML模型,用于CKD进展.
- ML模型显示出与KFRE竞争或超越功能衰竭预测的巨大潜力.
- 未来的ML模型开发可以增强早期检测和管理CKD进展.
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