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

Dynamic assessment of mortality risk in the intensive care unit (ICU) can beused to stratify patients, inform about treatment effectiveness or serve aspart of an early-warning system. Static risk scoring systems, such as APACHE orSAPS, have recently been supplemented with data-driven approaches that trackthe dynamic mortality risk over time. Recent works have focused on enhancingthe information delivered to clinicians even further by producing full survivaldistributions instead of point predictions or fixed horizon risks. In thiswork, we propose a non-parametric ensemble model, Weighted Resolution SurvivalEnsemble (WRSE), tailored to estimate such dynamic individual survivaldistributions. Inspired by the simplicity and robustness of ensemble methods,the proposed approach combines a set of binary classifiers spaced according toa decay function reflecting the relevance of short-term mortality predictions.Models and baselines are evaluated under weighted calibration anddiscrimination metrics for individual survival distributions which closelyreflect the utility of a model in ICU practice. We show competitive resultswith state-of-the-art probabilistic models, while greatly reducing trainingtime by factors of 2-9x.

WRSE -- a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU