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

We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), acomprehensive benchmark for evaluating foundation models in Emergency Medicineusing a dataset of 100K+ continuously monitored Emergency Department visitsfrom 2020-2022. MC-BEC focuses on clinically relevant prediction tasks attimescales from minutes to days, including predicting patient decompensation,disposition, and emergency department (ED) revisit, and includes a standardizedevaluation framework with train-test splits and evaluation metrics. Themultimodal dataset includes a wide range of detailed clinical data, includingtriage information, prior diagnoses and medications, continuously measuredvital signs, electrocardiogram and photoplethysmograph waveforms, orders placedand medications administered throughout the visit, free-text reports of imagingstudies, and information on ED diagnosis, disposition, and subsequent revisits.We provide performance baselines for each prediction task to enable theevaluation of multimodal, multitask models. We believe that MC-BEC willencourage researchers to develop more effective, generalizable, and accessiblefoundation models for multimodal clinical data.

Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine