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

In this work, we modify and apply self-supervision techniques to the domainof medical health insurance claims. We model patients' healthcare claimshistory analogous to free-text narratives, and introduce pre-trained `priorknowledge', later utilized for patient outcome predictions on a challengingtask: predicting Covid-19 hospitalization, given a patient's pre-Covid-19insurance claims history. Results suggest that pre-training on insurance claimsnot only produces better prediction performance, but, more importantly,improves the model's `clinical trustworthiness' and modelstability/reliability.

Self-supervision for health insurance claims data: a Covid-19 use case