在患有慢性病阶段3-5的患者中,个性化预测慢性病进展:使用机器学习方法的多中心研究
Trung Toan Duong1,2, Minh Tri Nguyen3, Chia Te Liao4,5,6
1International PhD program of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Studies in health technology and informatics
|August 8, 2025
概括
机器学习模型准确地预测慢性病 (CKD) 的进展. 轻GBM在预测估计质过率 (eGFR) 的下降方面表现强,有助于对CKD患者的早期干预.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 慢性病 (CKD) 是一种渐进的疾病,通常导致末期病 (ESRD).
- 准确预测 3-5 阶段的CKD进展对于及时干预和量身定制的治疗策略至关重要.
- 目前的预测方法可能缺乏有效的患者管理所需的精度.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测慢性病患者在一年内估计的球过率 (eGFR) 的下降.
- 通过ML解释性技术,识别驱动CKD进展的关键临床特征.
- 评估不同的ML算法,特别是LightGBM和Random Forest在预测eGFR下降方面的性能.
主要方法:
- 利用了来自台北医科大学临床研究数据库 (TMUCRD) 的大量CKD患者队列.
- 训练并比较机器学习模型,包括LightGBM和Random Forest,以预测eGFR下降.
- 采用SHAP (沙普利添加式解释) 分析来解释模型预测并识别重要特征.
主要成果:
- 轻GBM模型表现出卓越的性能,实现AUC值为0.76预测5%的eGFR下降和0.82预测25%的eGFR下降.
- 通过SHAP分析确定的关键预测因素包括基线eGFR,eGFR斜率和血液尿素 (BUN).
- 该研究证实了使用ML用于个性化预测CKD进展的可行性.
结论:
- 机器学习模型,特别是LightGBM,为预测CKD进展和eGFR衰退提供了强大的工具.
- 识别基线eGFR,eGFR斜率和BUN等关键预测特征可以实现更准确的风险分层.
- 这些发现支持将个性化ML驱动的预测模型集成到临床实践中,以改善CKD管理和患者结果.
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