Use of Natural Language Processing to Improve Identification of Patients With Peripheral Artery Disease.

ABSTRACT:

Background

Peripheral artery disease (PAD) is underrecognized, undertreated, and understudied: each of these endeavors requires efficient and accurate identification of patients with PAD. Currently, PAD patient identification relies on diagnosis/procedure codes or lists of patients diagnosed or treated by specific providers in specific locations and ways. The goal of this research was to leverage natural language processing to more accurately identify patients with PAD in an electronic health record system compared with a structured data-based approach.

Methods

The clinical notes from a cohort of 6861 patients in our health system whose PAD status had previously been adjudicated were used to train, test, and validate a natural language processing model using 10-fold cross-va

SUBMITTER: Weissler EH 

PROVIDER: S-EPMC7577538 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature