ID | 61534 |
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Author |
Ohara, Toshiaki
Department of Pathology & Experimental Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
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Ikeda, Hiroshi
Department of Internal Medicine, Shigei Medical Research Hospital
Sugitani, Yoshiki
Advanced Institute for Materials Research, Tohoku University
Suito, Hiroshi
Advanced Institute for Materials Research, Tohoku University
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Huynh, Viet Quang Huy
Advanced Institute for Materials Research, Tohoku University
Kinomura, Masaru
Division of Hemodialysis and Apheresis, Okayama University Hospital
Haraguchi, Soichiro
Kobayashi Medicine Clinic
Sakurama, Kazufumi
Department of Gastroenterological Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
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Abstract | Anemia, for which erythropoiesis-stimulating agents (ESAs) and iron supplements (ISs) are used as preventive measures, presents important difficulties for hemodialysis patients. Nevertheless, the number of physicians able to manage such medications appropriately is not keeping pace with the rapid increase of hemodialysis patients. Moreover, the high cost of ESAs imposes heavy burdens on medical insurance systems. An artificial-intelligence-supported anemia control system (AISACS) trained using administration direction data from experienced physicians has been developed by the authors. For the system, appropriate data selection and rectification techniques play important roles. Decision making related to ESAs poses a multi-class classification problem for which a two-step classification technique is introduced. Several validations have demonstrated that AISACS exhibits high performance with correct classification rates of 72%-87% and clinically appropriate classification rates of 92%-98%.
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Keywords | anemia
artificial intelligence
chronic kidney disease
erythropoiesis-stimulating agents
hemodialysis
iron
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Published Date | 2021-02-22
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Publication Title |
International Journal of Medical Sciences
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Volume | volume18
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Issue | issue8
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Publisher | Ivyspring International Publisher
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Start Page | 1831
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End Page | 1839
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ISSN | 1449-1907
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NCID | AA1197928X
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Content Type |
Journal Article
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language |
English
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OAI-PMH Set |
岡山大学
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Copyright Holders | © The author(s).
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File Version | publisher
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DOI | |
Related Url | isVersionOf https://www.medsci.org/v18p1831.htm
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License | https://creativecommons.org/licenses/by/4.0
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