Int J Med Sci 2021; 18(8):1831-1839. doi:10.7150/ijms.53298 This issue

Research Paper

Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients

Toshiaki Ohara1,2✉, Hiroshi Ikeda3, Yoshiki Sugitani4, Hiroshi Suito4, Viet Quang Huy Huynh4, Masaru Kinomura5, Soichiro Haraguchi6, Kazufumi Sakurama2,7

1. Department of Pathology & Experimental Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
2. Department of Gastroenterological Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
3. Department of Internal Medicine, Shigei Medical Research Hospital, Okayama, Japan.
4. Advanced Institute for Materials Research, Tohoku University, Miyagi, Japan.
5. Division of Hemodialysis and Apheresis, Okayama University Hospital, Okayama, Japan.
6. Kobayashi Medicine Clinic, Okayama, Japan.
7. Department of Dialysis Access Center, Shigei Medical Research Hospital, Okayama, Japan.

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Citation:
Ohara T, Ikeda H, Sugitani Y, Suito H, Huynh VQH, Kinomura M, Haraguchi S, Sakurama K. Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients. Int J Med Sci 2021; 18(8):1831-1839. doi:10.7150/ijms.53298. Available from https://www.medsci.org/v18p1831.htm

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Abstract

Graphic 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%.

Keywords: anemia, artificial intelligence, chronic kidney disease, erythropoiesis-stimulating agents, hemodialysis, iron