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使用可解释机器学习的活体脏捐赠后脏适应的预测工具
Junseok Jeon1, Jae Yong Yu2, Yeejun Song3,4
1Division of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Frontiers in medicine
|July 31, 2023
概括
一个新的机器学习模型预测活体捐赠后的功能. 这种工具有助于临床医生评估捐赠者脏捐赠后的适应性,确保更好的长期结果.
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
- 机器学习 机器学习
背景情况:
- 对活着的脏捐赠者来说,评估捐赠后的脏结果至关重要,因为预期寿命和并发症的差异很大.
- 预测适应对于供体安全和长期健康至关重要.
研究的目的:
- 开发一种可解释的机器学习模型,用于预测活体脏捐赠后的脏适应.
- 创建一个临床决策支持工具,用于评估经切除术后的捐赠功能.
主要方法:
- 利用基于机器学习的得分生成器AutoScore来构建预测模型.
- 包括2009-2020年823名活着的脏捐赠者,包括捐赠前变量,GFR和体积数据.
- 根据捐赠后估计的膜过率 (eGFR) 值,定义了公平和良好的适应.
主要成果:
- 开发的模型表现出对适应的良好预测能力.
- 在ROC曲线下实现的面积为0.846的公平适应和0.626的良好适应.
- 精确回忆曲线表现出强的表现,AUC分别为0.965和0.709.
结论:
- 捐赠后适应的预测工具表现出强大的预测性能.
- 开发了一个互动的,基于网络的应用程序,以帮助临床决策.
- 这个工具可以帮助临床医生管理活着的脏捐赠者,并优化捐赠后的护理.
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