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)

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 a ‘mult...

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Detalles Bibliográficos
Autores principales: Chen, Zhaoyi, Bird, Victoria Y., Ruchi, Rupam, Segal, Mark S., Bian, Jiang, Khan, Saeed R., Elie, Marie-Carmelle, Prosperi, Mattia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6098647/
https://www.ncbi.nlm.nih.gov/pubmed/30119627
http://dx.doi.org/10.1186/s12911-018-0652-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6098647/
https://www.ncbi.nlm.nih.gov/pubmed/30119627
http://dx.doi.org/10.1186/s12911-018-0652-4

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