Clinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation
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 prescri...
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Format: | Electronic Book Chapter |
Language: | English |
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Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2021
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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520 | |a 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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653 | |a diarrhea | ||
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653 | |a 16S rRNA sequencing | ||
653 | |a butyrate-producing bacteria | ||
653 | |a Proteobacteria | ||
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653 | |a allograft steatosis | ||
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653 | |a live donors | ||
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653 | |a Nephrology | ||
653 | |a CKD-MBD | ||
653 | |a CKD-Mineral and Bone Disorder | ||
653 | |a deceased donor | ||
653 | |a Eurotransplant Senior Program | ||
653 | |a risk stratification | ||
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653 | |a graft survival | ||
653 | |a prolonged ischaemic time | ||
653 | |a patient survival | ||
653 | |a pre-emptive transplantation | ||
653 | |a metabolomics | ||
653 | |a urine | ||
653 | |a acute rejection | ||
653 | |a allograft | ||
653 | |a cystatin C | ||
653 | |a hyperfiltration | ||
653 | |a kidney injury molecule (KIM)-1 | ||
653 | |a tubular damage | ||
653 | |a genetic polymorphisms | ||
653 | |a (cardiac) surgery | ||
653 | |a inflammatory cytokines | ||
653 | |a clinical studies | ||
653 | |a chronic kidney disease (CKD) | ||
653 | |a no known kidney disease (NKD) | ||
653 | |a ICD-10 billing codes | ||
653 | |a phenotyping | ||
653 | |a electronic health record (EHR) | ||
653 | |a estimated glomerular filtration rate (eGFR) | ||
653 | |a machine learning (ML) | ||
653 | |a generalized linear model network (GLMnet) | ||
653 | |a random forest (RF) | ||
653 | |a artificial neural network (ANN), clinical natural language processing (clinical NLP) | ||
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653 | |a fibrosis | ||
653 | |a extracellular matrix | ||
653 | |a collagen type VI | ||
653 | |a living-donor kidney transplantation | ||
653 | |a ethnic disparity | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/3882 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/76447 |7 0 |z DOAB: description of the publication |