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Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification
Volume 2, Issue / 2024
English Article 
Japanese Article- Please note that metadata of J-type articlesare generated by machine-translation and the original texts are written in Japanese.
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Foreword 花房規男 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 2024. |
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Than the grasp of the needs by possibility - literature review of the AI inflection in the nurse supporting haemodialysis patients of our country - 日向野香織1, 佐藤真由美2 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 1-9, 2024. |
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Practice of the AI model construction by the multi-type of job cooperation 遠藤太一 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 11-14, 2024. |
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Future - of the dialysis that examination of pro-generation AI and the efficiency predictive model based on the machine learning and applied - data lead 森實篤司 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 15-20, 2024. |
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Construction of the AI support diagnosis model for vascular access evaluations 星子清貴1, 長岡剛史3, 森石みさき3, 川西秀樹2 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 21-26, 2024. |
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Search of the functional peptide utilizing the artificial intelligence 濱田浩幸1, 弘胤智美1, 兼廣健吾1, 花井泰三1, 友雅司2, 金成泰3,4, 山下明泰5 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 27-31, 2024. |
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An unsolved problem in the haemodialysis therapy and expectation to AI 岩藤和広1, 吉田智史1, 中曽瑛子2, 和泉一樹2, 陣内彦博2 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 33-39, 2024. |
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Expect it in JAINBP from the field of medical engineering 山下明泰1,2 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 41-44, 2024. |
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About implementation and the current situation in MML for standardization and joint ownership of the dialysis medical care information 鈴木卓1, 菅原正純2 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 45-52, 2024. |
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Examination of the hypotension predictive model using the machine learning 川崎路浩 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 53-58, 2024. |
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Expectations for predicting clearance of hemodialysis using machine learning 崎山亮一1,2, 今田賢心1, 野地聖2 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 59-63, 2024. |
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AI-Driven Transformation in Next Generation Medical Practice for Kidney and Blood Purification 花房規男 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 65-68, 2024. |
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Dimensionality compression using UMAP method 花房規男 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 69-70, 2024. |
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The issue of data disproportion and the SMOTE method 花房規男 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 71-71, 2024. |
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About implementation and the current situation in MML for standardization and joint ownership of the dialysis medical care information 鈴木卓1, 菅原正純2 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 73-73, 2024. |
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Practice of the AI model construction by the multi-type of job cooperation 遠藤太一1,2 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 73-73, 2024. |
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Construction of the hypotension predictive model using the machine learning to prevent low blood pressure in the dialysis 川崎路浩, 鈴木聡 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 73-73, 2024. |
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The future of the dialysis that examination of pro-generation AI and the efficiency predictive model based on the machine learning and applied - data lead 森實篤司 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 73-74, 2024. |
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Examination of construction and the implementation of the VAIVT classification machine learning model by the echo 星子清貴1, 長岡剛史2, 森石みさき2, 川西秀樹3 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 74-74, 2024. |
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Expectation to a clearance prediction of the haemodialysis using the machine learning 崎山亮一1,2, 野地聖2 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 74-74, 2024. |
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Examination of the shunts sound visualization system using the electrostethoscope 長沼俊秀, 新健太郎, 武本佳昭, 内田潤次 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 74-74, 2024. |
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Search of the antibiotic resistance protein utilizing the artificial intelligence 濱田浩幸1,5, 長野拓也1, 友雅司2,5, 金成泰3,5, 山下明泰4,5 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 74-75, 2024. |
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Expect it in JAINBP from the field of medical engineering 山下明泰 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 75-75, 2024. |
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Expect it from dialysis medical care, medicine in JAINBP 友雅司 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 75-75, 2024. |
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An ethic and intellectual property in medical equipment research and development to implement AI 正木崇生 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 75-75, 2024. |
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Practice in the next-generation kidney, hemocatharsis domain that AI clears 花房規男 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 75-76, 2024. |
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The prospects of the AI domain in the α1-microglobulin study 井上朋子1, 金成泰2, 花房規男3, 濱田浩幸4, 岩藤和広3, 鶴田悠木5 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 76-76, 2024. |
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An unsolved problem in the haemodialysis therapy and expectation to AI 岩藤和広1, 中曽瑛子2, 和泉一樹2, 陣内彦博2 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 76-76, 2024. |
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Construction and evaluation of the blood pressure estimated personal model based on the face image of haemodialysis patients using the AI 大岩孝輔1, 鈴木聡2, 前田佳孝3, 陣内彦博4 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 76-76, 2024. |
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About the DX introduction to a dialysis system and the practice 松下翔二郎 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 76-77, 2024. |
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We will elaborate a plan with an example of DX which JMS pushes forward in the future 吉田真一, 小林重樹, 山下哲以 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 77-77, 2024. |
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Data utilization and the task which we read at a point of view called the automation of the dialysis treatment 辻和也 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 77-77, 2024. |
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Introduction of "IoT maintenance service DiaXrs(R)" utilizing DX which NIPRO provides 市川勇貴 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 77-78, 2024. |
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Than the grasp of the needs by possibility - literature review of the AI inflection in the nurse supporting haemodialysis patients of our country ... 日向野香織1, 佐藤真由美2 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 78-78, 2024. |
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Examination of the usefulness of the dimension compression by UMAP (Uniform Manifold Approximation and Projection) 花房規男, 井上貴博, 川口祐輝, 矢島愛治, 土谷健 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 78-78, 2024. |
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About a prediction of anemia treatment in patients on dialysis using the machine learning 井上貴博1,2, 花房規男1, 川口祐輝1, 矢島愛治1, 土谷健1 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 78-78, 2024. |
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Examination of the data expansion for data disproportionate to the attempt of the dry weight change prediction using the machine learning method 花房規男, 井上貴博, 川口祐輝, 矢島愛治, 土谷健 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification 2: 78-79, 2024. |
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