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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The shift to electronic health records has created a plethora of information ready to be examined and acted upon by those in the medical and computational fields. While this allows for novel research on a scale unthinkable in the past, all discoveries still rely on some initial insight leading to a hypothesis. As the size and variety of data grows so do the number of potential findings, making it necessary to optimize hypothesis generation to increase the rate and importance of discoveries produced from the data. By using distributed Association Rule Mining and Contrast Mining in a big data ecosystem, it is possible to discover discrepancies within large, complex populations which are inaccessible using traditional methods. These discrepancies, when used as hypotheses, can help improve patient care through decision support, population health analytics, and other areas of healthcare.

Contrast Mining for Pattern Discovery and Descriptive Analytics to Tailor Sub-Groups of Patients Using Big Data Solutions