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

by   Joshua C. Chang, et al.

We developed an inherently interpretable multilevel Bayesian framework for representing variation in regression coefficients that mimics the piecewise linearity of ReLU-activated deep neural networks. We used the framework to formulate a survival model for using medical claims to predict hospital readmission and death that focuses on discharge placement, adjusting for confounding in estimating causal local average treatment effects. We trained the model on a 5% sample of Medicare beneficiaries from 2008 and 2011, based on their 2009–2011 inpatient episodes, and then tested the model on 2012 episodes. The model scored an AUROC of approximately 0.76 on predicting all-cause readmissions (defined using official 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, we provide what blackboxes cannot – the exact gold-standard global interpretation of the model, identifying relative risk factors and quantifying the effect of discharge placement. We also show that the posthoc explainer SHAP fails to provide accurate explanations.


page 6

page 7

page 11

page 13

page 14

page 15

page 16

page 17


Explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction

Alzheimer's disease and related dementias (ADRD) ranks as the sixth lead...

CDSM – Casual Inference using Deep Bayesian Dynamic Survival Models

A smart healthcare system that supports clinicians for risk-calibrated t...

Interpretable Mixture of Experts for Structured Data

With the growth of machine learning for structured data, the need for re...

Semi-Structured Deep Piecewise Exponential Models

We propose a versatile framework for survival analysis that combines adv...

Suicide Risk Modeling with Uncertain Diagnostic Records

Motivated by the pressing need for suicide prevention through improving ...

Learning Prescriptive ReLU Networks

We study the problem of learning optimal policy from a set of discrete t...

Please sign up or login with your details

Forgot password? Click here to reset