Using natural language processing of clinical text to enhance identification of opioid-related overdoses in electronic health records data.

ABSTRACT:

Purpose

To enhance automated methods for accurately identifying opioid-related overdoses and classifying types of overdose using electronic health record (EHR) databases.

Methods

We developed a natural language processing (NLP) software application to code clinical text documentation of overdose, including identification of intention for self-harm, substances involved, substance abuse, and error in medication usage. Using datasets balanced with cases of suspected overdose and records of individuals at elevated risk for overdose, we developed and validated the application using Kaiser Permanente Northwest data, then tested portability of the application using Kaiser Permanente Washington data. Datasets were chart-reviewed to provide a gold standard for comparison and evaluati

SUBMITTER: Hazlehurst B 

PROVIDER: S-EPMC6772185 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature