Výsledky vyhledávání

Use of Deep Learning to Predict Acute Kidney Injury After Intravenous Contrast Media Administration: Prediction Model Development Study (Preprint)

Autor: Donghwan Yun, Semin Cho, Yong C Kim, Dong K Kim, Kook-Hwan Oh, Kwon W Joo, Yon S Kim, Seung S Han

BACKGROUND Precise prediction of contrast media–induced acute kidney injury (CIAKI) is an important issue because of its relationship with poor outcomes. OBJECTIVE Herein, we examined whether a deep learning algorithm could predict the risk of intr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6307216e8f7d3381414192a3d5ef7d44
https://doi.org/10.2196/preprints.27177

Zobrazit plný text záznamu

Real-time conditional reliability prediction from on-line tool performance data

Autor: William J. Kolarik, Yon S. Kim

Publikováno v: International Journal of Production Research. 30:1831-1844

This paper presents results from research aimed at developing a real-time, ‘look ahead’ reliability measure of risk relative to tool wear or breakage. The objective is to present results of a successful research effort which has developed a real-

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7a7c38230d94c355b859c052e6a23cc0
https://doi.org/10.1080/00207549208948125

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání