Predicting Kidney Transplant Survival using Multiple Feature Representations for HLAs
Predicting Kidney Transplant Survival using Multiple Feature Representations for HLAs
复制标题
使用 HLA 的多个特征表示预测肾移植存活率
DOI：
10.1007/978-3-030-77211-6_6
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Kevin S. Xu
中科院分区：
文献类型：
--
作者：
Mohammadreza Nemati;Haonan Zhang;Michael Sloma;D. Bekbolsynov;Hong Wang;S. Stepkowski;Kevin S. Xu
肾移植可以显著提高终末期肾病患者的生活水平。肾移植中影响移植物存活时间（移植失败和患者需要另一次移植的时间）的一个重要因素是供体和受体之间的人类白细胞抗原（hla）的相容性。在本文中，我们提出了将HLA信息纳入基于机器学习的生存分析算法的新的生物学相关特征表示。我们在超过100,000例移植的数据库中评估了我们提出的HLA特征表示，发现它们将预测准确性提高了约1%，在患者水平上是适度的，但在社会水平上可能具有重要意义。准确预测存活时间可以改善移植存活结果，使供体更好地分配给受者，并减少由于供体匹配不良导致移植失败而再次移植的数量。
Kidney transplantation can significantly enhance living standards for people suffering from end-stage renal disease. A significant factor that affects graft survival time (the time until the transplant fails and the patient requires another transplant) for kidney transplantation is the compatibility of the Human Leukocyte Antigens (HLAs) between the donor and recipient. In this paper, we propose new biologically-relevant feature representations for incorporating HLA information into machine learning-based survival analysis algorithms. We evaluate our proposed HLA feature representations on a database of over 100,000 transplants and find that they improve prediction accuracy by about 1%, modest at the patient level but potentially significant at a societal level. Accurate prediction of survival times can improve transplant survival outcomes, enabling better allocation of donors to recipients and reducing the number of re-transplants due to graft failure with poorly matched donors.
影响因子：
5.8
作者：
Simon N;Friedman J;Hastie T;Tibshirani R
通讯作者：
Tibshirani R