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

Readmission rates in the hospitals are increasingly being used as a benchmarkto determine the quality of healthcare delivery to hospitalized patients.Around three-fourths of all hospital re-admissions can be avoided, savingbillions of dollars. Many hospitals have now deployed electronic health record(EHR) systems that can be used to study issues that trigger readmission.However, most of the EHRs are high dimen-sional and sparsely populated, andanalyzing such data sets is a Big Data challenge. The effect of some of thewell-known dimension reduction techniques is minimized due to presence ofnon-linear variables. We use association mining as a dimension reduction methodand the results are used to develop models, using data from an existing nursingEHR system, for predicting risk of re-admission to the hospitals. These modelscan help in determining effective treatments for patients to minimize thepossibility of re-admission, bringing down the cost and increasing the qualityof care provided to the patients. Results from the models show significantlyaccurate predictions of patient re-admission.

Predicting Hospital Re-admissions from Nursing Care Data of Hospitalized Patients