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

The use of deep neural models for diagnosis prediction from clinical text hasshown promising results. However, in clinical practice such models must notonly be accurate, but provide doctors with interpretable and helpful results.We introduce ProtoPatient, a novel method based on prototypical networks andlabel-wise attention with both of these abilities. ProtoPatient makespredictions based on parts of the text that are similar to prototypicalpatients - providing justifications that doctors understand. We evaluate themodel on two publicly available clinical datasets and show that it outperformsexisting baselines. Quantitative and qualitative evaluations with medicaldoctors further demonstrate that the model provides valuable explanations forclinical decision support.

This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text