用机器学习生成的个性化预测模型,用于在活着脏捐赠一年后对脏功能进行预测
Rikako Oki1,2, Toshihio Hirai3, Kazuhiro Iwadoh4
1Department of Organ Transplant Medicine, Tokyo Women's Medical University, Shinjuku City, Tokyo, Japan.
Scientific reports
|July 2, 2025
概括
机器学习准确地预测了捐赠后一年活着的脏捐赠者的功能. 该工具使用手术前的数据来估计血清肌素,改善个性化的捐赠者护理和监测.
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
- 生物医学工程 生物医学工程
背景情况:
- 活体脏捐赠通常会导致功能减少30%左右.
- 捐赠后功能的个体变化需要个性化监测.
- 准确预测捐赠后的功能对于捐赠者的福祉至关重要.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于预测捐赠后一年血清肌素 (Cre) 水平.
- 使用手术前临床数据,包括体积测量,进行预测.
- 加强对活体脏捐赠者的个性化护理和监测.
主要方法:
- 通过遗传编程开发了一种使用符号回归的ML模型.
- 包括204名活着的脏捐赠者,以及手术前临床数据和CT衍生体积.
- 使用7:3训练测试数据分割验证模型.
主要成果:
- 该ML模型实现了0.079 mg/dL的中位数绝对误差,用于预测Cre.
- 在验证队列中表现优于传统方法 (较高的R2,较低的RMSE和MAE).
- 确定的主要预测变量是手术前的CRE和残留体积.
结论:
- 开发的ML模型准确地预测了捐赠后的功能.
- 该模型可以帮助监测捐赠者的结果,并加强个性化护理.
- 作为一个实用临床实用性的Web应用程序部署.
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