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

Recently we proposed the Span Attribute Tagging (SAT) Model (Du et al., 2019)to infer clinical entities (e.g., symptoms) and their properties (e.g.,duration). It tackles the challenge of large label space and limited trainingdata using a hierarchical two-stage approach that identifies the span ofinterest in a tagging step and assigns labels to the span in a classificationstep.  We extend the SAT model to jointly infer not only entities and theirproperties but also relations between them. Most relation extraction modelsrestrict inferring relations between tokens within a few neighboring sentences,mainly to avoid high computational complexity. In contrast, our proposedRelation-SAT (R-SAT) model is computationally efficient and can infer relationsover the entire conversation, spanning an average duration of 10 minutes.  We evaluate our model on a corpus of clinical conversations. When theentities are given, the R-SAT outperforms baselines in identifying relationsbetween symptoms and their properties by about 32% (0.82 vs 0.62 F-score) andby about 50% (0.60 vs 0.41 F-score) on medications and their properties. On themore difficult task of jointly inferring entities and relations, the R-SATmodel achieves a performance of 0.34 and 0.45 for symptoms and medicationsrespectively, which is significantly better than 0.18 and 0.35 for the baselinemodel. The contributions of different components of the model are quantifiedusing ablation analysis.

Learning to Infer Entities, Properties and their Relations from Clinical Conversations