机器学习模型的开发和验证,以预测慢性病患者的替代疗法的时间
Jun Okita1, Takeshi Nakata2, Hiroki Uchida1
1Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan.
BMC nephrology
|March 17, 2024
概括
机器学习模型准确地预测慢性病 (CKD) 患者的替代疗法 (RRT) 的时间. 这种方法显著优于依赖于估计的膜过率 (eGFR) 下降率的传统方法.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 预测替代疗法 (RRT) 的时间对于管理末期病风险较高的患者至关重要.
- 目前的预测方法通常依赖于估计的膜过率 (eGFR) 下降的速度.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测RRT的时间.
- 将ML模型的准确性与使用eGFR下降率的传统预测方法进行比较.
主要方法:
- 利用了135名成年慢性病 (CKD) 患者进行血液透析的电子病历.
- 开发和评估了多个ML模型,包括最小绝对收缩和选择操作员 (LASSO) 回归,使用培训和验证数据集.
- 将ML模型准确度 (R2) 与基于eGFR下降率的传统方法进行比较.
主要成果:
- 拉索回归模型实现了中等准确度,确定系数 (R2) 为0.60.
- 使用eGFR下降率的传统预测方法显示精度非常低 (R2 = -17.1).
结论:
- 机器学习模型可以使用单个时间点数据适度预测到RRT的时间.
- 这种ML方法与依赖于eGFR时间序列数据的传统方法相比,显示出更高的性能.
- 这些发现表明了优化CKD治疗策略的新可能性.
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