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

Relying on large pretrained language models such as Bidirectional EncoderRepresentations from Transformers (BERT) for encoding and adding a simpleprediction layer has led to impressive performance in many clinical naturallanguage processing (NLP) tasks. In this work, we present a novel extension tothe Transformer architecture, by incorporating signature transform with theself-attention model. This architecture is added between embedding andprediction layers. Experiments on a new Swedish prescription data show theproposed architecture to be superior in two of the three information extractiontasks, comparing to baseline models. Finally, we evaluate two differentembedding approaches between applying Multilingual BERT and translating theSwedish text to English then encode with a BERT model pretrained on clinicalnotes.

Information Extraction from Swedish Medical Prescriptions with Sig-Transformer Encoder