文献
J-GLOBAL ID:201802215193005917
整理番号:18A1067902
潜在変数モデリングは伝統的方法と比較したAKIリスク因子同定とAKI予測を改善する【JST・京大機械翻訳】
Latent variable modeling improves AKI risk factor identification and AKI prediction compared to traditional methods
著者 (7件):
Smith Loren E.
(Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, USA)
,
Smith Derek K.
(Department of Biostatistics, Vanderbilt University Medical Center, Nashville, USA)
,
Blume Jeffrey D.
(Department of Biostatistics, Vanderbilt University Medical Center, Nashville, USA)
,
Siew Edward D.
(Department of Medicine, Vanderbilt University Medical Center, Nashville, USA)
,
Siew Edward D.
(Division of Nephrology and Hypertension, Vanderbilt Center for Kidney Disease and Integrated Program for AKI Research, Vanderbilt University Medical Center, Nashville, USA)
,
Billings Frederic T.
(Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, USA)
,
Billings Frederic T.
(Department of Medicine, Vanderbilt University Medical Center, Nashville, USA)
資料名:
BMC Nephrology (Web)
(BMC Nephrology (Web))
巻:
18
号:
1
ページ:
55
発行年:
2017年
JST資料番号:
U7437A
ISSN:
1471-2369
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
イギリス (GBR)
言語:
英語 (EN)