Reprint
Clinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation
Edited by
June 2021
374 pages
- ISBN978-3-0365-1134-4 (Hardback)
- ISBN978-3-0365-1135-1 (PDF)
This is a Reprint of the Special Issue Clinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation that was published in
Medicine & Pharmacology
Public Health & Healthcare
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.
Format
- Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
tacrolimus; C/D ratio; tacrolimus metabolism; everolimus; conversion; kidney transplantation; gut microbiome; renal transplant recipient; diarrhea; immunosuppressive medication; gut microbiota; kidney transplantation; 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; acute kidney injury; patent ductus arteriosus; conservative management; blood pressure; eradication; interferon-free regimen; hepatitis C infection; kidney transplant; acute kidney injury; allograft steatosis; lipopeliosis; kidney transplantation; transplant numbers; live donors; public awareness; Google TrendsTM; artificial intelligence; machine learning; big data; nephrology; transplantation; kidney transplantation; acute kidney injury; chronic kidney disease; NLR; PLR; RPGN; predictive value; hemodialysis; withdrawal; cellular crescent; global sclerosis; procurement kidney biopsy; glomerulosclerosis; kidney transplantation; transplantation; outcomes; minimally-invasive donor nephrectomy; robot-assisted surgery; laparoscopic surgery; kidney transplantation; organ donation; living kidney donation; MeltDose®; LCPT; tacrolimus; renal function; liver transplantation; C/D ratio; metabolism; erythropoietin; fibroblast growth factor 23; death; kidney transplantation; acute kidney injury; weekend effect; in-hospital mortality; comorbidity; dialysis; elderly; klotho; α-Klotho; FGF-23; kidney transplantation; kidney donor; renal transplantation; transplantation; Nephrology; CKD-MBD; CKD-Mineral and Bone Disorder; kidney transplantation; organ donation; deceased donor; Eurotransplant Senior Program; risk stratification; intensive care; nephrology; kidney transplant; 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; kidney transplantation; metabolomics; immunosuppression; urine; acute rejection; immunosuppression; allograft; acute kidney injury; cystatin C; hyperfiltration; kidney injury molecule (KIM)-1; tubular damage; acute kidney injury; genetic polymorphisms; risk prediction; (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); kidney transplantation; fibrosis; inflammation; extracellular matrix; collagen type VI; living kidney donation; living-donor kidney transplantation; ethnic disparity