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 present an interpretable neural network for predicting an importantclinical outcome (1-year mortality) from multi-modal Electronic Health Record(EHR) data. Our approach builds on prior multi-modal machine learning models bynow enabling visualization of how individual factors contribute to the overalloutcome risk, assuming other factors remain constant, which was previouslyimpossible.  We demonstrate the value of this approach using a large multi-modal clinicaldataset including both EHR data and 31,278 echocardiographic videos of theheart from 26,793 patients. We generated separate models for (i) clinical dataonly (CD) (e.g. age, sex, diagnoses and laboratory values), (ii) numericvariables derived from the videos, which we call echocardiography-derivedmeasures (EDM), and (iii) CD+EDM+raw videos (pixel data). The interpretablemulti-modal model maintained performance compared to non-interpretable models(Random Forest, XGBoost), and also performed significantly better than a modelusing a single modality (average AUC=0.82). Clinically relevant insights andmulti-modal variable importance rankings were also facilitated by the newmodel, which have previously been impossible.

Interpretable Neural Networks for Predicting Mortality Risk using Multi-modal Electronic Health Records