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

Abstract

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.

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

“…Those needs that are classified as unmet require provision of some ample spaces for the purpose of imagination in relation to leveraging the strength associated with big data, as well as relevant artificial intelligence (AI) to improve the overall status of patients with kidney diseases [25]. In this article, we discuss the big data concepts in nephrology, describe the potential use of AI in nephrology and transplantation, and also encourage researchers and clinicians to submit their invaluable research, including original clinical research studies [26][27][28][29][30], database studies from registries [31][32][33], meta-analyses [34][35][36][37][38][39][40][41][42][43][44], and artificial intelligence research [25,[45][46][47][48] in nephrology and transplantation. Table 1 demonstrates known and commonly used databases that have provided big data in nephrology and transplantation [49][50][51].…”

Augmenting the Transplant Team With Artificial Intelligence: Toward Meaningful AI Use in Solid Organ Transplant

“…There are several excellent reviews of how AI may be applied in transplant (7,8), and about how different AI methods work (9). Briefly, two broad types of AI relevant to transplant are rulesbased expert systems and machine learning systems.…”

Toward Advancing Long-Term Outcomes of Kidney Transplantation with Artificial Intelligence

“…Nonetheless, all AI-based approaches need data, often large sets of accurate data for training, validation, and testing. International efforts to create large datasets are expected to accelerate future advancements in the kidney transplantation field, and to pave the way toward alleviating the longstanding burden of adverse long-term outcomes post-kidney transplantation [9,10]. Such efforts require sufficient funding for setting up the architectures and communication systems that allow timely extraction and storage of the data, as well as for maintaining and potentially updating the resulting databases.…”