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 records of a clinical encounter can be extensive and complex, thusplacing a premium on tools that can extract and summarize relevant information.This paper introduces the task of generating discharge summaries for a clinicalencounter. Summaries in this setting need to be faithful, traceable, and scaleto multiple long documents, motivating the use of extract-then-abstractsummarization cascades. We introduce two new measures, faithfulness andhallucination rate for evaluation in this task, which complement existingmeasures for fluency and informativeness. Results across seven medical sectionsand five models show that a summarization architecture that supportstraceability yields promising results, and that a sentence-rewriting approachperforms consistently on the measure used for faithfulness(faithfulness-adjusted $F_3$) over a diverse range of generated sections.

Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes