Identifying Patients With Peripheral Artery Disease Using the Electronic Health Record: A Pragmatic Approach.

ABSTRACT:

Background

Peripheral artery disease (PAD) is underdiagnosed due to poor patient and clinician awareness. Despite this, no widely accepted PAD screening is recommended.

Objectives

The authors used machine learning to develop an automated risk stratification tool for identifying patients with a high likelihood of PAD.

Methods

Using data from the electronic health record (EHR), ankle-brachial indices (ABIs) were extracted for 3,298 patients. In addition to ABI, we extracted 60 other patient characteristics and used a random forest model to rank the features by association with ABI. The model identified several features independently correlated with PAD. We then built a logistic regression model to predict PAD status on a validation set of patients (n = 1,089), an extern

SUBMITTER: Sonderman M 

PROVIDER: S-EPMC10569163 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature