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4. Clinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation.

Author

Cheungpasitporn, Wisit, Cheungpasitporn, Wisit, Kaewput, Wisit, and Thongprayoon, Charat

Subjects
Medicine, (cardiac) surgery, 16S rRNA sequencing, C/D ratio, CKD-MBD, CKD-Mineral and Bone Disorder, Eurotransplant Senior Program, FGF-23, Goodpasture syndrome, Google TrendsTM, ICD-10 billing codes, LCPT, MeltDose®, NLR, Nephrology, PLR, Proteobacteria, RPGN, acute kidney injury, acute rejection, allograft, allograft steatosis, anti-GBM disease, area under the precision-recall curve (AUCPR), area under the receiver operating characteristic (AUROC), artificial intelligence, artificial neural network (ANN), clinical natural language processing (clinical NLP), big data, blood pressure, bone mineral density, butyrate-producing bacteria, cardiovascular mortality, cellular crescent, chronic kidney disease, chronic kidney disease (CKD), clinical studies, collagen type VI, comorbidity, conservative management, conversion, cystatin C, death, deceased donor, dialysis, diarrhea, discharge summaries, dual-energy X-ray absorptiometry, elderly, electronic health record (EHR), epidemiology, eradication, erythropoietin, estimated glomerular filtration rate (eGFR), ethnic disparity, everolimus, extracellular matrix, fibroblast growth factor 23, fibrosis, generalized linear model network (GLMnet), genetic polymorphisms, global sclerosis, glomerulosclerosis, graft failure, graft survival, gut microbiome, gut microbiota, hemodialysis, hepatitis C infection, hospitalization, hyperfiltration, immunosuppressed host, immunosuppression, immunosuppressive medication, in-hospital mortality, inflammation, inflammatory cytokines, intensive care, interferon-free regimen, kidney donor, kidney injury molecule (KIM)-1, kidney transplant, kidney transplant recipients, kidney transplantation, klotho, laboratory values, laparoscopic surgery, lifestyle, lipopeliosis, live donors, liver transplantation, living donation, living kidney donation, living-donor kidney transplantation, long-term outcomes, machine learning, machine learning (ML), metabolism, metabolomics, minimally-invasive donor nephrectomy, nephrology, no known kidney disease (NKD), organ donation, outcome, outcomes, patent ductus arteriosus, patient survival, phenotyping, pre-emptive transplantation, predictive value, procurement kidney biopsy, prolonged ischaemic time, public awareness, random forest (RF), renal function, renal transplant recipient, renal transplantation, repeated kidney transplantation, risk prediction, risk stratification, robot-assisted surgery, tacrolimus, tacrolimus metabolism, torquetenovirus, transplant numbers, transplantation, tubular damage, urine, vascular calcification, weekend effect, withdrawal, α-Klotho
Abstract

Summary: In recent years, artificial intelligence has increasingly been playing an essential role in diverse areas in medicine, assisting clinicians in patient management. In nephrology and transplantation, artificial intelligence can be utilized to enhance clinical care, such as through hemodialysis prescriptions and the follow-up of kidney transplant patients. Furthermore, there are rapidly expanding applications and validations of comprehensive, computerized medical records and related databases, including national registries, health insurance, and drug prescriptions. For this Special Issue, we made a call to action to stimulate researchers and clinicians to submit their invaluable works and present, here, a collection of articles covering original clinical research (single- or multi-center), database studies from registries, meta-analyses, and artificial intelligence research in nephrology including acute kidney injury, electrolytes and acid-base, chronic kidney disease, glomerular disease, dialysis, and transplantation that will provide additional knowledge and skills in the field of nephrology and transplantation toward improving patient outcomes.

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