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

Text descriptions in natural language are an essential part of electronic health records (EHRs). Such descriptions usually contain facts about patient’s life, events, diseases and other relevant information. Sometimes it may also include facts about their family members. In order to find the facts about the right person (experiencer) and convert the unstructured medical text into structured information, we developed a module of experiencer detection. We compared different vector representations and machine learning models to get the highest quality of 0.96 f-score for binary classification and 0.93 f-score for multi-classification. Additionally, we present the results plotting the family disease tree.

Experiencer Detection and Automated Extraction of a Family Disease Tree from Medical Texts in Russian Language