Díez-Sanmartín Covadonga, Sarasa Cabezuelo Antonio
Department of Computer Systems and Computing, School of Computer Science, Complutense University of Madrid, 28040 Madrid, Spain.
J Clin Med. 2020 Feb 19;9(2):572. doi: 10.3390/jcm9020572.
A key issue in the field of kidney transplants is the analysis of transplant recipients' survival. By means of the information obtained from transplant patients, it is possible to analyse in which cases a transplant has a higher likelihood of success and the factors on which it will depend. In general, these analyses have been conducted by applying traditional statistical techniques, as the amount and variety of data available about kidney transplant processes were limited. However, two main changes have taken place in this field in the last decade. Firstly, the digitalisation of medical information through the use of electronic health records (EHRs), which store patients' medical histories electronically. This facilitates automatic information processing through specialised software. Secondly, medical Big Data has provided access to vast amounts of data on medical processes. The information currently available on kidney transplants is huge and varied by comparison to that initially available for this kind of study. This new context has led to the use of other non-traditional techniques more suitable to conduct survival analyses in these new conditions. Specifically, this paper provides a review of the main machine learning methods and tools that are being used to conduct kidney transplant patient and graft survival analyses.
肾移植领域的一个关键问题是对移植受者的生存情况进行分析。通过从移植患者那里获得的信息，可以分析出在哪些情况下移植成功的可能性更高以及移植成功所依赖的因素。一般来说，由于关于肾移植过程的可用数据数量和种类有限，这些分析一直是通过应用传统统计技术来进行的。然而，在过去十年中，该领域发生了两个主要变化。首先，通过使用电子健康记录（EHRs）实现了医疗信息的数字化，电子健康记录以电子方式存储患者的病史。这便于通过专门软件进行自动信息处理。其次，医疗大数据提供了获取大量医疗过程数据的途径。与最初可用于此类研究的数据相比，目前有关肾移植的信息量大且种类繁多。这种新情况导致使用其他更适合在这些新条件下进行生存分析的非传统技术。具体而言，本文对用于进行肾移植患者和移植物生存分析的主要机器学习方法和工具进行了综述。
