Clinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation
Cheungpasitporn, Wisit (editor)
Thongprayoon, Charat (editor)
Kaewput, Wisit (editor)
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.
Keywordstacrolimus; C/D ratio; tacrolimus metabolism; everolimus; conversion; kidney transplantation; gut microbiome; renal transplant recipient; diarrhea; immunosuppressive medication; gut microbiota; 16S rRNA sequencing; butyrate-producing bacteria; Proteobacteria; torquetenovirus; immunosuppression; transplantation; immunosuppressed host; outcome; renal transplantation; Goodpasture syndrome; anti-GBM disease; epidemiology; hospitalization; outcomes; acute kidney injury; risk prediction; artificial intelligence; patent ductus arteriosus; conservative management; blood pressure; eradication; interferon-free regimen; hepatitis C infection; kidney transplant; allograft steatosis; lipopeliosis; transplant numbers; live donors; public awareness; Google TrendsTM; machine learning; big data; nephrology; chronic kidney disease; NLR; PLR; RPGN; predictive value; hemodialysis; withdrawal; cellular crescent; global sclerosis; procurement kidney biopsy; glomerulosclerosis; minimally-invasive donor nephrectomy; robot-assisted surgery; laparoscopic surgery; organ donation; living kidney donation; MeltDose®; LCPT; renal function; liver transplantation; metabolism; erythropoietin; fibroblast growth factor 23; death; weekend effect; in-hospital mortality; comorbidity; dialysis; elderly; klotho; α-Klotho; FGF-23; kidney donor; Nephrology; CKD-MBD; CKD-Mineral and Bone Disorder; deceased donor; Eurotransplant Senior Program; risk stratification; intensive care; kidney transplant recipients; long-term outcomes; graft failure; cardiovascular mortality; lifestyle; inflammation; vascular calcification; bone mineral density; dual-energy X-ray absorptiometry; living donation; repeated kidney transplantation; graft survival; prolonged ischaemic time; patient survival; pre-emptive transplantation; metabolomics; urine; acute rejection; allograft; cystatin C; hyperfiltration; kidney injury molecule (KIM)-1; tubular damage; genetic polymorphisms; (cardiac) surgery; inflammatory cytokines; clinical studies; chronic kidney disease (CKD); no known kidney disease (NKD); ICD-10 billing codes; phenotyping; electronic health record (EHR); estimated glomerular filtration rate (eGFR); machine learning (ML); generalized linear model network (GLMnet); random forest (RF); artificial neural network (ANN), clinical natural language processing (clinical NLP); discharge summaries; laboratory values; area under the receiver operating characteristic (AUROC); area under the precision-recall curve (AUCPR); fibrosis; extracellular matrix; collagen type VI; living-donor kidney transplantation; ethnic disparity
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Publication date and placeBasel, Switzerland, 2021