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Citing this Article

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Published on 05.04.16 in Vol 4, No 2 (2016): Apr-Jun

This paper is in the following e-collection/theme issue:

Works citing "A Querying Method over RDF-ized Health Level Seven v2.5 Messages Using Life Science Knowledge Resources"

Yoshimasa Kawazoe, Takeshi Imai, Kazuhiko Ohe

JMIR Med Inform 2016 (Apr 05); 4(2):e12 

According to Crossref, the following articles are citing this article (DOI 10.2196/medinform.5275):

(note that this is only a small subset of citations)

  1. Nakagawa N, Sofue T, Kanda E, Nagasu H, Matsushita K, Nangaku M, Maruyama S, Wada T, Terada Y, Yamagata K, Narita I, Yanagita M, Sugiyama H, Shigematsu T, Ito T, Tamura K, Isaka Y, Okada H, Tsuruya K, Yokoyama H, Nakashima N, Kataoka H, Ohe K, Okada M, Kashihara N. J-CKD-DB: a nationwide multicentre electronic health record-based chronic kidney disease database in Japan. Scientific Reports 2020;10(1)
    CrossRef
  2. Natsiavas P, Malousi A, Bousquet C, Jaulent M, Koutkias V. Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches. Frontiers in Pharmacology 2019;10
    CrossRef
  3. Chepelev L, Giannopoulos A, Tang A, Mitsouras D, Rybicki FJ. Medical 3D printing: methods to standardize terminology and report trends. 3D Printing in Medicine 2017;3(1)
    CrossRef
  4. Freedman HG, Williams H, Miller MA, Birtwell D, Mowery DL, Stoeckert CJ. A novel tool for standardizing clinical data in a semantically rich model. Journal of Biomedical Informatics 2020;112:100086
    CrossRef
  5. Sofue T, Nakagawa N, Kanda E, Nagasu H, Matsushita K, Nangaku M, Maruyama S, Wada T, Terada Y, Yamagata K, Narita I, Yanagita M, Sugiyama H, Shigematsu T, Ito T, Tamura K, Isaka Y, Okada H, Tsuruya K, Yokoyama H, Nakashima N, Kataoka H, Ohe K, Okada M, Kashihara N, Shimosawa T. Prevalences of hyperuricemia and electrolyte abnormalities in patients with chronic kidney disease in Japan: A nationwide, cross-sectional cohort study using data from the Japan Chronic Kidney Disease Database (J-CKD-DB). PLOS ONE 2020;15(10):e0240402
    CrossRef
  6. Nagasu H, Yano Y, Kanegae H, Heerspink HJ, Nangaku M, Hirakawa Y, Sugawara Y, Nakagawa N, Tani Y, Wada J, Sugiyama H, Tsuruya K, Nakano T, Maruyama S, Wada T, Yamagata K, Narita I, Tamura K, Yanagita M, Terada Y, Shigematsu T, Sofue T, Ito T, Okada H, Nakashima N, Kataoka H, Ohe K, Okada M, Itano S, Nishiyama A, Kanda E, Ueki K, Kashihara N. Kidney Outcomes Associated With SGLT2 Inhibitors Versus Other Glucose-Lowering Drugs in Real-world Clinical Practice: The Japan Chronic Kidney Disease Database. Diabetes Care 2021;44(11):2542
    CrossRef
  7. Yano Y, Nagasu H, Kanegae H, Nangaku M, Hirakawa Y, Sugawara Y, Nakagawa N, Wada J, Sugiyama H, Nakano T, Wada T, Shimizu M, Suzuki H, Komatsu H, Nakashima N, Kitaoka K, Narita I, Okada H, Suzuki Y, Kashihara N. Kidney outcomes associated with haematuria and proteinuria trajectories among patients with IgA nephropathy in real‐world clinical practice: The Japan Chronic Kidney Disease Database. Nephrology 2024;29(2):65
    CrossRef
  8. Kishi S, Nakashima T, Goto T, Nagasu H, Brooks CR, Okada H, Tamura K, Nakano T, Narita I, Maruyama S, Yano Y, Yokoo T, Wada T, Wada J, Nangaku M, Kashihara N. Association of serum magnesium levels with renal prognosis in patients with chronic kidney disease. Clinical and Experimental Nephrology 2024;
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/medinform.5275):

  1. El Guemhioui K, Demurjian SA. Data Analytics in Medicine. 2020. chapter 29:528
    CrossRef