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 developed an inherently interpretable multilevel Bayesian framework forrepresenting variation in regression coefficients that mimics the piecewiselinearity of ReLU-activated deep neural networks. We used the framework toformulate a survival model for using medical claims to predict hospitalreadmission and death that focuses on discharge placement, adjusting forconfounding in estimating causal local average treatment effects. We trainedthe model on a 5% sample of Medicare beneficiaries from 2008 and 2011, based ontheir 2009--2011 inpatient episodes, and then tested the model on 2012episodes. The model scored an AUROC of approximately 0.76 on predictingall-cause readmissions -- defined using official Centers for Medicare andMedicaid Services (CMS) methodology -- or death within 30-days of discharge,being competitive against XGBoost and a Bayesian deep neural network,demonstrating that one need-not sacrifice interpretability for accuracy.Crucially, as a regression model, we provide what blackboxes cannot -- theexact gold-standard global interpretation of the model, identifying relativerisk factors and quantifying the effect of discharge placement. We also showthat the posthoc explainer SHAP fails to provide accurate explanations.

Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death