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Review

Application of Artificial Intelligence Techniques to Predict Survival in Kidney Transplantation: A Review

Department of Computer Systems and Computing, School of Computer Science, Complutense University of Madrid, 28040 Madrid, Spain
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J. Clin. Med. 2020, 9(2), 572; https://doi.org/10.3390/jcm9020572
Received: 29 January 2020 / Revised: 17 February 2020 / Accepted: 18 February 2020 / Published: 19 February 2020
(This article belongs to the Special Issue The Future of Artificial Intelligence in Clinical Medicine)
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. View Full-Text
Keywords: artificial intelligence; machine learning; survival; kidney transplantation artificial intelligence; machine learning; survival; kidney transplantation
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MDPI and ACS Style

Díez-Sanmartín, C.; Sarasa Cabezuelo, A. Application of Artificial Intelligence Techniques to Predict Survival in Kidney Transplantation: A Review. J. Clin. Med. 2020, 9, 572. https://doi.org/10.3390/jcm9020572

AMA Style

Díez-Sanmartín C, Sarasa Cabezuelo A. Application of Artificial Intelligence Techniques to Predict Survival in Kidney Transplantation: A Review. Journal of Clinical Medicine. 2020; 9(2):572. https://doi.org/10.3390/jcm9020572

Chicago/Turabian Style

Díez-Sanmartín, Covadonga; Sarasa Cabezuelo, Antonio. 2020. "Application of Artificial Intelligence Techniques to Predict Survival in Kidney Transplantation: A Review" J. Clin. Med. 9, no. 2: 572. https://doi.org/10.3390/jcm9020572

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