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Page 1
Predicting dry weight change in Hemodialysis patients using machine learning.
Inoue H, Oya M, Aizawa M, Wagatsuma K, Kamimae M, Kashiwagi Y, Ishii M, Wakabayashi H, Fujii T, Suzuki S, Hattori N, Tatsumoto N, Kawakami E, Asanuma K. Inoue H, et al. Among authors: kawakami e. BMC Nephrol. 2023 Jun 29;24(1):196. doi: 10.1186/s12882-023-03248-5. BMC Nephrol. 2023. PMID: 37386392 Free PMC article.
Predicting pathological highly invasive lung cancer from preoperative [18F]FDG PET/CT with multiple machine learning models.
Onozato Y, Iwata T, Uematsu Y, Shimizu D, Yamamoto T, Matsui Y, Ogawa K, Kuyama J, Sakairi Y, Kawakami E, Iizasa T, Yoshino I. Onozato Y, et al. Among authors: kawakami e. Eur J Nucl Med Mol Imaging. 2023 Feb;50(3):715-726. doi: 10.1007/s00259-022-06038-7. Epub 2022 Nov 17. Eur J Nucl Med Mol Imaging. 2023. PMID: 36385219 Free PMC article.
Sodium-glucose co-transporter 2 inhibitor therapy reduces the administration frequency of anti-vascular endothelial growth factor agents in patients with diabetic macular oedema with a history of anti-vascular endothelial growth factor agent use: A cohort study using the Japanese health insurance claims database.
Ishibashi R, Inaba Y, Koshizaka M, Takatsuna Y, Tatsumi T, Shiko Y, Kashiwagi Y, Maezawa Y, Kawasaki Y, Kawakami E, Yamamoto S, Yokote K. Ishibashi R, et al. Among authors: kawakami e. Diabetes Obes Metab. 2024 Apr;26(4):1510-1518. doi: 10.1111/dom.15454. Epub 2024 Jan 19. Diabetes Obes Metab. 2024. PMID: 38240052
Sodium-glucose cotransporter 2 inhibitor therapy reduces the administration frequency of steroid injection in patients with diabetic macular edema: A cohort study using the Japanese health insurance claims database.
Ishibashi R, Koshizaka M, Takatsuna Y, Tatsumi T, Maezawa Y, Shiko Y, Inaba Y, Kawasaki Y, Kashiwagi Y, Kawakami E, Yamamoto S, Yokote K. Ishibashi R, et al. Among authors: kawakami e. J Diabetes Investig. 2024 Sep;15(9):1231-1238. doi: 10.1111/jdi.14253. Epub 2024 Jun 14. J Diabetes Investig. 2024. PMID: 38874094 Free PMC article.
Comparison of machine-learning and logistic regression models for prediction of 30-day unplanned readmission in electronic health records: A development and validation study.
Iwagami M, Inokuchi R, Kawakami E, Yamada T, Goto A, Kuno T, Hashimoto Y, Michihata N, Goto T, Shinozaki T, Sun Y, Taniguchi Y, Komiyama J, Uda K, Abe T, Tamiya N. Iwagami M, et al. Among authors: kawakami e. PLOS Digit Health. 2024 Aug 20;3(8):e0000578. doi: 10.1371/journal.pdig.0000578. eCollection 2024 Aug. PLOS Digit Health. 2024. PMID: 39163277 Free PMC article.
332 results