Articles of Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification | Volume , Issue suppl / 2025

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Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification

Volume , Issue suppl / 2025

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Japanese Article

Please note that metadata of J-type articlesare generated by machine-translation and the original texts are written in Japanese.

	Next Stage greetings of the fifth Japan kidney, hemocatharsis AI association arts and sciences meeting, general meeting kidney, hemocatharsis 正木崇生 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 3-3, 2025.

	SL1. Possibility of kidney, the hemocatharsis medical care that omen control AI founds 伊東大輔 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 23-23, 2025.

	EL1. AI basic knowledge for studies 濱田浩幸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 (suppl): 24-24, 2025.

	EL2. Clinical application and the prospects of the artificial intelligence in the kidney hemocatharsis domain and the large-scale language model 神田英一郎 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 25-25, 2025.

	EL3. Use of the generation AI depending on a use 岩藤和広1), 吉田智史1), 和泉一樹2), 陣内彦博2) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 26-26, 2025.

	WS1-1. The current situation of digital transformation (DX) in the dialysis medical care and expectation to the future 菊地勘 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 27-27, 2025.

	WS1-2. Future image of the dialysis medical care by AI/DX 森實篤司 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 28-28, 2025.

	WS2-1. Application to the haemodialysis management of the open source machine learning library 井上貴博1), 平野一1), 町村哲郎1), 宗像優1), 森友実1), 花房規男1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 29-29, 2025.

	WS2-2. We utilize AI and predict the frailty in haemodialysis patients 高橋蓮1), 矢部広樹2) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 30-30, 2025.

	WS2-3. Prediction of the hypotension in the dialysis using the AI analysis of the face expression change 大釜健広1), 大岩孝輔2), 鈴木聡3), 前田佳孝4), 野澤昭雄5), 佐久間宏治1), 内田明子1), 石塚俊治1), 佐藤純彦1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 31-31, 2025.

	O1-1. Attempt of the machine learning model construction utilizing the AI assistant support by the abecedarian 木谷博之1), 中村健一1), 昌木秀介1), 栗栖啓子2), 藤野早知栄3), 内藤隆之3), 烏田一義4), 西澤欣子5) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 32-32, 2025.

	O1-2. Consideration of the tent-formed T wave judgment by wearable device and AutoEncoder 祝前俊輝, 島崎拓則 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 32-32, 2025.

	O1-3. Construction of the machine learning model predicting prognosis from clinical information at vascular access making for one year 松岡佑季, 末光浩太郎 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 32-32, 2025.

	O2-1. The morphologic aging degree that AI estimated from a kidney supersonic wave image predicts kidney prognosis independently 原悠1,2), 藤田雅子1), 森本靖久1), 森崇寧1), 須佐紘一郎1), 飯盛聡一郎1), 内藤省太郎1), 蘇原映誠1), 内田信一1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 33-33, 2025.

	O2-2. Application of the machine learning model in the cardiovascular surgery surgery definitive kidney alternative therapy introduction risk prediction 星子清貴, 上田彰, 山口裕司, 荒川保雄 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 33-33, 2025.

	O2-3. Development of the dialysis low blood pressure predictive model that we can interpret using feature engineering 二宮大地1), 久保田志織2,3), 中村俊平2), 正木崇生3) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 33-33, 2025.

	O2-4. It is the construction of the predictive model of the potassium level and clinical intervention after the dialysis 田部井陸斗1), 小久保謙一1), 長沼俊秀2), 酒井利奈1), 新健太郎3), 小林こず恵1), 吉田和弘1), 久保田勝1), 内田潤次2), 氏平政伸1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 33-33, 2025.

O2-4. It is the construction of the predictive model of the potassium level and clinical intervention after the dialysis
田部井陸斗1), 小久保謙一1), 長沼俊秀2), 酒井利奈1), 新健太郎3), 小林こず恵1), 吉田和弘1), 久保田勝1), 内田潤次2), 氏平政伸1)
Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 33-33, 2025.

	O2-5. Performance evaluation of various models in the Hb level prediction of haemodialysis patients 辻村和哉1), 南條浩輝1), 池之上辰義2) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 34-34, 2025.

	O2-6. Attempt of the death event prediction of patients on dialysis using Deep Learning 福士謙1), 尹重浩1), 川口俊明1), 北川柾彦1), 吉田孝博2), 畠山真吾3) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 34-34, 2025.

	O2-7. The development of the machine learning model predicting the frailty onset two years in foreign haemodialysis patients later: Multicenter study 高橋蓮1,2), 矢部広樹3), 石川英昭4), 日比野貴志1), 瀧聞佳菜1), 松井明由奈1), 森山善文5), 山田哲也6) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 34-34, 2025.

O2-7. The development of the machine learning model predicting the frailty onset two years in foreign haemodialysis patients later: Multicenter study
高橋蓮1,2), 矢部広樹3), 石川英昭4), 日比野貴志1), 瀧聞佳菜1), 松井明由奈1), 森山善文5), 山田哲也6)
Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 34-34, 2025.

	O2-8. Examination of the optimization of the bleeding incident detection algorithm using the depths learning 下田佳志1), 吉田未来2), 島崎拓則1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 34-34, 2025.

	O3-1. Use experience for the valvular disease of the heart using the phonocardiography device 佐藤瞬, 渡邉出, 上野彰之, 佐藤眞由美, 黒田浩樹, 虫賀督幸, 加藤碧 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 35-35, 2025.

	O3-2. Development of the OCR cooperation clothes image display system aiming at human error reduction 中野薫, 鈴木隆斗, 佐藤眞由美, 上野彰之, 渡邊出 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 35-35, 2025.

	O3-3. A knowledge base plan and the prospects to the hemocatharsis domain that - GrammarXiv suggests from tacit knowledge to meeting intellect 遠藤陶子 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 35-35, 2025.

	O3-4. About AI utilization for the work-style reform in the pharmaceutical company 尾花山和哉 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 35-35, 2025.

	O3-5. Is the information readable 100 years later? Anxiety ... of ... End-of-Life Software 田中宏志 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 36-36, 2025.

	O3-6. Examination of the Hb cycling inhibition by the feedback inhibition simulation 土井瞭1), 土井俊樹2,3), 森井健一2,3), 西澤欣子2), 正木崇生3) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 36-36, 2025.

	O3-7. Practice and evaluation of the generation AI utilization in the multi-type of job cooperation education about patients on dialysis 川合真子1), 野口佑太2), 八重徹司3), 吉子健一4) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 36-36, 2025.

	O4-1. The machine learning distinction of the shunts function (bloodstream, resistive index) by the shunts sound analysis: Examination of the useful sound feature 吉田駿1), 山村恒平1), 小佐野慧一1), 大越貴絵1), 四方美穂1), 石井俊史1), 小西真樹子1), 高橋和也1), 秋山大一郎2), 中島歩1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 37-37, 2025.

O4-1. The machine learning distinction of the shunts function (bloodstream, resistive index) by the shunts sound analysis: Examination of the useful sound feature
吉田駿1), 山村恒平1), 小佐野慧一1), 大越貴絵1), 四方美穂1), 石井俊史1), 小西真樹子1), 高橋和也1), 秋山大一郎2), 中島歩1)
Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 37-37, 2025.

	O4-2. Evaluation (the second report) of dialysis shunts stenosis using the wireless small device 菊池達也1), 小泉美香子1), 片桐大輔1), 大谷良介2), 峯啓真2), 高野恭行3), 高野秀樹1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 37-37, 2025.

	O4-3. Comparison of the shunts function predictive model by the integration of shunts sound signature and the clinical information 吉田駿1), 山村恒平1), 小佐野慧一1), 大越貴絵1), 四方美穂1), 石井俊史1), 小西真樹子1), 高橋和也1), 秋山大一郎2), 中島歩1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 37-37, 2025.

O4-3. Comparison of the shunts function predictive model by the integration of shunts sound signature and the clinical information
吉田駿1), 山村恒平1), 小佐野慧一1), 大越貴絵1), 四方美穂1), 石井俊史1), 小西真樹子1), 高橋和也1), 秋山大一郎2), 中島歩1)
Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 37-37, 2025.

	O4-4. Basic examination of the AI judgment by the imaging of the blood vessel stertor 崎山亮一1), 新健太郎2), 長沼俊秀3) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 37-37, 2025.

	O4-5. Construction of the risk model predicting the early internal shunt imperfection of haemodialysis patients 大久保愛子1), 森井健一1,2), 土井俊樹1,2), 西澤欣子2), 山下和臣2), 正木崇生1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 38-38, 2025.

	O4-6. Development of the α1-MG extraction coefficient predictive model using the ensemble learning 西田英樹1), 森石みさき1), 高橋直子2), 河端宏幸1), 真島菜々子1), 土谷晋一郎1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 38-38, 2025.

	O4-7. Proteinuria remission prediction after the palatine tonsil enucleation + steroid pulse therapy for the IgA nephropathy using the machine learning 久保田志織1), 二宮大地2), 前岡侑二郎1), 佐々木健介1), 正木崇生1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 38-38, 2025.

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