论文标题
使用机器学习的肾脏移植移植物存活的预测模型
A predictive model for kidney transplant graft survival using machine learning
论文作者
论文摘要
肾脏移植是终末期肾衰竭患者的最佳治疗方法。用于肾脏质量评估的主要方法是基于COX回归的肾脏供体风险指数。机器学习方法可以改善移植结果的预测并帮助决策。对基于树的机器学习方法，随机森林，接受了与最初用于开发风险指数相同的数据进行培训和评估（1995- 2005年的70,242个观测值）。随机森林成功地预测了与II型错误率相等10％的风险指数的额外2148次移植。使用Kaplan-Meier分析在移植后长达240个月的随访生存结果分析预测的结果，并确认随机森林的表现明显优于风险指数（p <0.05）。随机森林预测的是，与风险指数相比，移植更加成功和更长的移植。随机森林和其他机器学习模型可以改善移植决策。
Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney donor risk index. A machine learning method may provide improved prediction of transplant outcomes and help decision-making. A popular tree-based machine learning method, random forest, was trained and evaluated with the same data originally used to develop the risk index (70,242 observations from 1995-2005). The random forest successfully predicted an additional 2,148 transplants than the risk index with equal type II error rates of 10%. Predicted results were analyzed with follow-up survival outcomes up to 240 months after transplant using Kaplan-Meier analysis and confirmed that the random forest performed significantly better than the risk index (p<0.05). The random forest predicted significantly more successful and longer-surviving transplants than the risk index. Random forests and other machine learning models may improve transplant decisions.
