English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

AEGIS study possesses unique information on longitudinal changes incirculating glucose through continuous glucose monitoring technology (CGM).However, as usual in longitudinal medical studies, there is a significantamount of missing data in the outcome variables. For example, 40 percent ofglycosylated hemoglobin (A1C) biomarker data are missing five years ahead. Withthe purpose to reduce the impact of this issue, this article proposes a newdata analysis framework based on learning in reproducing kernel Hilbert spaces(RKHS) with missing responses that allows to capture non-linear relationsbetween variable studies in different supervised modeling tasks. First, weextend the Hilbert-Schmidt dependence measure to test statistical independencein this context introducing a new bootstrap procedure, for which we proveconsistency. Next, we adapt or use existing models of variable selection,regression, and conformal inference to obtain new clinical findings aboutglucose changes five years ahead with the AEGIS data. The most relevantfindings are summarized below: i) We identify new factors associated withlong-term glucose evolution; ii) We show the clinical sensibility of CGM datato detect changes in glucose metabolism; iii) We can improve clinicalinterventions based on our algorithms' expected glucose changes according topatients' baseline characteristics.

Glucose values prediction five years ahead with a new framework of  missing responses in reproducing kernel Hilbert spaces, and the use of  continuous glucose monitoring technology