文献
J-GLOBAL ID:202202274600109748
整理番号:22A1089741
糖尿病予防プログラムからの個別化治療効果を予測する電子健康記録適合モデル:臨床試験と実世界データを用いた交差証拠合成アプローチ【JST・京大機械翻訳】
An Electronic Health Record-Compatible Model to Predict Personalized Treatment Effects From the Diabetes Prevention Program: A Cross-Evidence Synthesis Approach Using Clinical Trial and Real-World Data
著者 (9件):
Kent David M.
(Predictive Analytics and Comparative Effectiveness Center, Tufts Medical Center, Boston, MA)
,
Nelson Jason
(Predictive Analytics and Comparative Effectiveness Center, Tufts Medical Center, Boston, MA)
,
Pittas Anastassios
(Division of Endocrinology, Tufts Medical Center, Boston, MA)
,
Colangelo Francis
(Allegheny Health Network, Pittsburgh, PA)
,
Koenig Carolyn
(Mercy Health, St. Louis, MO)
,
van Klaveren David
(Predictive Analytics and Comparative Effectiveness Center, Tufts Medical Center, Boston, MA)
,
van Klaveren David
(Department of Public Health, Erasmus MC University Medical Center, Rotterdam, the Netherlands)
,
Ciemins Elizabeth
(American Medical Group Association, Alexandria, VA)
,
Cuddeback John
(American Medical Group Association, Alexandria, VA)
資料名:
Mayo Clinic Proceedings
(Mayo Clinic Proceedings)
巻:
97
号:
4
ページ:
703-715
発行年:
2022年
JST資料番号:
W3137A
ISSN:
0025-6196
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
オランダ (NLD)
言語:
英語 (EN)