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

Efficient representation of patients is very important in the healthcaredomain and can help with many tasks such as medical risk prediction. Manyexisting methods, such as diagnostic Cost Groups (DCG), rely on expertknowledge to build patient representation from medical data, which is resourceconsuming and non-scalable. Unsupervised machine learning algorithms are a goodchoice for automating the representation learning process. However, there isvery little research focusing on onpatient-level representation learningdirectly from medical claims. In this paper, weproposed a novel patient vectorlearning architecture that learns high quality,fixed-length patientrepresentation from claims data. We conducted several experiments to test thequality of our learned representation, and the empirical results show that ourlearned patient vectors are superior to vectors learned through other methodsincluding a popular commercial model. Lastly, we provide potential clinicalinterpretation for using our representation on predictive tasks, asinterpretability is vital in the healthcare domain

Distributed representation of patients and its use for medical cost prediction