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/

Zendy Plus

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

Zendy Tools

Zendy Open

Hospital readmission is a crucial healthcare quality measure that helps indetermining the level of quality of care that a hospital offers to a patientand has proven to be immensely expensive. It is estimated that more than $25billion are spent yearly due to readmission of diabetic patients in the USA.This paper benchmarks existing models and proposes a new embedding basedstate-of-the-art deep neural network(DNN). The model can identify whether ahospitalized diabetic patient will be readmitted within 30 days or not with anaccuracy of 95.2% and Area Under the Receiver Operating Characteristics(AUROC)of 97.4% on data collected from 130 US hospitals between 1999-2008. The resultsare encouraging with patients having changes in medication while admittedhaving a high chance of getting readmitted. Identifying prospective patientsfor readmission could help the hospital systems in improving their inpatientcare, thereby saving them from unnecessary expenditures.

EmbPred30: Assessing 30-days Readmission for Diabetic Patients using Categorical Embeddings