Neural network models for predicting readmission among patients undergoing peripheral vascular intervention using electronic health record data and clinical registry data.

ABSTRACT:

Objectives

To determine whether neural network models based on electronic health record (EHR) data can match and augment the performance of models based on clinical registry data in predicting readmission after peripheral vascular intervention (PVI).

Design

Observational cohort study.

Setting

Vascular Quality Initiative registry and INSIGHT Clinical Research Network EHR data from multiple academic institutions in New York City.

Participants

Patients undergoing PVI during January 1, 2013 to September 30, 2021.

Main outcome measures

Our outcome variable was 90-day readmission. We developed logistic regression (LR), multilevel perceptron (MLP), and recurrent neural network (RNN) models using registry alone, EHR data alone, and combined registry-EHR data. EH

SUBMITTER: Mao J 

PROVIDER: S-EPMC12207173 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature