The impact of electronic health record discontinuity on prediction modeling.

ABSTRACT:

Background

To determine the impact of electronic health record (EHR)-discontinuity on the performance of prediction models.

Methods

The study population consisted of patients with a history of cardiovascular (CV) comorbidities identified using US Medicare claims data from 2007 to 2017, linked to EHR from two networks (used as model training and validation set, respectively). We built models predicting one-year risk of mortality, major CV events, and major bleeding events, stratified by high vs. low algorithm-predicted EHR-continuity. The best-performing models for each outcome were chosen among 5 commonly used machine-learning models. We compared model performance by Area under the ROC curve (AUROC) and Area under the precision-recall curve (AUPRC).

Results

Based on 1

SUBMITTER: Kar S 

PROVIDER: S-EPMC10325091 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature