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

We propose a novel semiparametric model for the joint distribution of acontinuous longitudinal outcome and the baseline covariates using an enrichedDirichlet process (EDP) prior. This joint model decomposes into a linear mixedmodel for the outcome given the covariates and marginals for the covariates.The nonparametric EDP prior is placed on the regression and splinecoefficients, the error variance, and the parameters governing the predictorspace. We predict the outcome at unobserved time points for subjects with dataat other time points as well as for new subjects with only baseline covariates.We find improved prediction over mixed models with Dirichlet process (DP)priors when there are a large number of covariates. Our method is demonstratedwith electronic health records consisting of initiators of second generationantipsychotic medications, which are known to increase the risk of diabetes. Weuse our model to predict laboratory values indicative of diabetes for eachindividual and assess incidence of suspected diabetes from the predicteddataset. Our model also serves as a functional clustering algorithm in whichsubjects are clustered into groups with similar longitudinal trajectories ofthe outcome over time.

Outcome identification in electronic health records using predictions from an enriched Dirichlet process mixture