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

E. Hope Weissler, Jikai Zhang, Steven Lippmann, Shelley Rusincovitch, Ricardo Henao, W. Schuyler Jones

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

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-validation. The performance of this model was described using the area under the receiver operating characteristic and average precision curves; its performance was quantitatively compared with an administrative data-based least absolute shrinkage and selection operator (LASSO) approach using the DeLong test. Results: The median (SD) of the area under the receiver operating characteristic curve for the natural language processing model was 0.888 (0.009) versus 0.801 (0.017) for the LASSO-based approach alone (DeLong P
Original languageEnglish (US)
JournalCirculation: Cardiovascular Interventions
Volume13
Issue number10
DOIs
StatePublished - Oct 1 2020
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-25

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine
  • General Medicine

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