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

The hospital discharge summaryis an essential document, containing clinical and administrative information necessary for the continuity of care after the patients are discharged from hospital. The utilization of electronic discharge summaries has grown in popularity. However, many transcription errors and spelling mistakes exit, potentially reducing the medical quality of patient care. To solve this problem, this paper presents a novel approach to detect these errors automatically by using Named entity recognition (NER). The NER model was trained by 450 discharge summaries and rich features set was used to improve the recall and precision. Experiment on the independent test set validated the good performance of NER. The follow-up error detection using the trained NER discovered that the mistakes and ambiguous information that frequently occurred in discharge summaries. Keywords-named entity recognition; natural language processing; electronic discharge summary; error detectio; conditional random field

A Method to Detect Errors in Electronic Discharge Summaries Based on Named Entity Recognition