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AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science|June 23, 2023
Interpretable Stratification for Chronic Kidney Disease Progression Based on Time to Event AnalysisMohamed Ghalwash, Akira Koseki, Toshiya Iwamori, et al.
Clinical and Experimental Nephrology|March 1, 2022
Prescription rate of erythropoietin-stimulating agents is low for patients with renal impairment under non-nephrology care in a tertiary-level academic medical center in JapanNaoki Okamoto, Daijo Inaguma, Hiroki Hayashi, et al.
Plos One|September 17, 2020
Increasing tendency of urine protein is a risk factor for rapid eGFR decline in patients with CKD: A machine learning-based prediction model by using a big databaseDaijo Inaguma, Akimitsu Kitagawa, Ryosuke Yanagiya, et al.
BMJ Open|June 9, 2022
Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospective cohort study using a large data set from a hospital in JapanDaijo Inaguma, Hiroki Hayashi, Ryosuke Yanagiya, et al.
Studies in Health Technology and Informatics|April 22, 2018
Risk Prediction of Diabetic Nephropathy via Interpretable Feature Extraction from EHR Using Convolutional AutoencoderTakayuki Katsuki, Masaki Ono, Akira Koseki, et al.
BMC Nephrology|May 12, 2026
Multimodal predictions of end stage chronic kidney disease from asymptomatic individuals for discovery of genomic biomarkersSimona Rabinovici-Cohen, Daniel E Platt, Toshiya Iwamori, et al.
Scientific Reports|November 25, 2024
Investigating the impact of steroid dependence on gastrointestinal surgical outcomes from UK BiobankUri Kartoun, Akira Koseki, Akihiro Kosugi, et al.
Scientific Reports|August 16, 2019
Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learningMasaki Makino, Ryo Yoshimoto, Masaki Ono, et al.
Proceedings of the National Academy of Sciences of the United States of America|March 20, 2026
Quantifying the fidelity of in vitro human cell culture systems using a biomedical foundation modelSatoru Fujii, Scott T Espenschied, Vibha Anand, et al.
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