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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Accurately predicting hospital length-of-stay at the time a patient isadmitted to hospital may help guide clinical decision making and resourceallocation. In this study we aim to build a decision support system thatpredicts hospital length-of-stay for patients admitted to general internalmedicine from the emergency department. We conduct an exploratory data analysisand employ feature selection methods to identify the attributes that result inthe best predictive performance. We also develop a discrete-event simulationmodel to assess the performances of the prediction models in a practicalsetting. Our results show that the recommendation performances of the proposedapproaches are generally acceptable and do not benefit from the featureselection. Further, the results indicate that hospital length-of-stay could bepredicted with reasonable accuracy (e.g., AUC value for classifying short andlong stay patients is 0.69) using patient admission demographics, laboratorytest results, diagnostic imaging, vital signs and clinical documentation.

Assessing the impact of emergency department short stay units using length-of-stay prediction and discrete event simulation