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

Clinical decision support tools rooted in machine learning and optimizationcan provide significant value to healthcare providers, including through bettermanagement of intensive care units. In particular, it is important that thepatient discharge task addresses the nuanced trade-off between decreasing apatient's length of stay (and associated hospitalization costs) and the risk ofreadmission or even death following the discharge decision. This workintroduces an end-to-end general framework for capturing this trade-off torecommend optimal discharge timing decisions given a patient's electronichealth records. A data-driven approach is used to derive a parsimonious,discrete state space representation that captures a patient's physiologicalcondition. Based on this model and a given cost function, an infinite-horizondiscounted Markov decision process is formulated and solved numerically tocompute an optimal discharge policy, whose value is assessed using off-policyevaluation strategies. Extensive numerical experiments are performed tovalidate the proposed framework using real-life intensive care unit patientdata.

Optimal discharge of patients from intensive care via a data-driven policy learning framework