Large-scale identification of patients with cerebral aneurysms using natural language processing.

ABSTRACT:

Objective

To use natural language processing (NLP) in conjunction with the electronic medical record (EMR) to accurately identify patients with cerebral aneurysms and their matched controls.

Methods

ICD-9 and Current Procedural Terminology codes were used to obtain an initial data mart of potential aneurysm patients from the EMR. NLP was then used to train a classification algorithm with .632 bootstrap cross-validation used for correction of overfitting bias. The classification rule was then applied to the full data mart. Additional validation was performed on 300 patients classified as having aneurysms. Controls were obtained by matching age, sex, race, and healthcare use.

Results

We identified 55,675 patients of 4.2 million patients with ICD-9 and Current Procedural

SUBMITTER: Castro VM 

PROVIDER: S-EPMC5224711 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature