Department of Pathology & Experimental Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
Department of Gastroenterological Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
Int J Med Sci. 2021 Feb 22;18(8):1831-1839. doi: 10.7150/ijms.53298. eCollection 2021.
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%.
贫血是血液透析患者面临的一个重要问题,常使用促红细胞生成素(ESA)和铁剂(IS)进行预防。然而,能够正确管理这些药物的医生数量并没有跟上血液透析患者数量的快速增长。此外,ESA 的高成本给医疗保险系统带来了沉重的负担。作者开发了一种基于人工智能的贫血控制系统(AISACS),该系统使用经验丰富的医生的管理方向数据进行训练。对于该系统,合适的数据选择和校正技术起着重要作用。与 ESA 相关的决策是一个多类分类问题,引入了两步分类技术。几项验证表明,AISACS 具有较高的性能,正确分类率为 72%-87%,临床适当分类率为 92%-98%。