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Updated: May 22, 2025
TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
Published on: April 13, 2021
Current Applications and Developments of Natural Language Processing in Kidney Transplantation: A Scoping Review
René Gatsinga1, Rachel Shu-En Lau1, Benjamin Jia Han Lim1
1Department of Urology, Singapore General Hospital, Singapore, Singapore.
Background And Objective:
Natural language processing (NLP) is a subfield of artificial intelligence that enables computers to process human language. As most human interactions today involve the internet and electronic devices, NLP tools quickly become indispensable to modern life. The use of NLP tools in medical practice and research is growing fast. This scoping review evaluates the current and potential future applications of NLP in kidney transplantation medicine.
Design:
We conducted an electronic literature search on NLP in the setting of kidney transplantation on PubMed, EMBASE, and Scopus from inception to August 26, 2024. Two independent investigators conducted the search strategy and reviewed abstracts and full texts; conflicts were resolved after discussion with a third and fourth author. A total of ten studies were included in the study.
Results:
The most commonly studied clinical applications of NLP in kidney transplantation are its use as an adjunct tool to facilitate early diagnosis of renal disease and as an effective predictor of graft loss and complications among kidney transplant recipients. Some researchers were able to predict organs at risk of delayed implant or discard by analyzing donors' EHR; this has the potential to improve organ utilization significantly. In clinical research, NLP tools can be tailored to perform specific tasks of interest on unstructured text. By studying n comments from social media and news websites, 1 group was able to gauge public perception of transplant policies and identify potential actions to improve access to transplant care.
Conclusions:
NLP tools have only recently been introduced into medical research, but they are already significantly impacting kidney transplantation medicine. The literature demonstrates the potential to improve early diagnosis of renal failure, predict renal transplantation outcomes, improve organ utilization, and support advocacy and policymaking. With more widespread use of EHR globally and the continued development of NLP technology, these tools are poised to revolutionize the practice of renal transplantation.
