Development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical, demographics and laboratory data: the diagnostic acute care algorithm - kidney stones (DACA-KS)

Development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical, demographics and laboratory data: the diagnostic acute care algorithm - kidney stones (DACA-KS)

Abstract Background Kidney stone (KS) disease has high, increasing prevalence in the United States and poses a massive economic burden. Diagnostics algorithms of KS only use a few variables with a limited sensitivity and specificity. In this study, we tested a big data approach to infer and validate...

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Bibliographic Details
Main Authors: Zhaoyi Chen, Victoria Y. Bird, Rupam Ruchi, Mark S. Segal, Jiang Bian, Saeed R. Khan, Marie-Carmelle Elie, Mattia Prosperi
Format: Article
Language:English
Published: BMC 2018-08-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Diagnostic algorithm
Kidney stones
Big data analysis
Online Access:http://link.springer.com/article/10.1186/s12911-018-0652-4

Internet

http://link.springer.com/article/10.1186/s12911-018-0652-4

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