Identifying and classifying opioid-related overdoses: A validation study.

ABSTRACT:

Purpose

The study aims to develop and validate algorithms to identify and classify opioid overdoses using claims and other coded data, and clinical text extracted from electronic health records using natural language processing (NLP).

Methods

Primary data were derived from Kaiser Permanente Northwest (2008-2014), an integrated health care system (~n > 475 000 unique individuals per year). Data included International Classification of Diseases, Ninth Revision (ICD-9) codes for nonfatal diagnoses, International Classification of Diseases, Tenth Revision (ICD-10) codes for fatal events, clinical notes, and prescription medication records. We assessed sensitivity, specificity, positive predictive value, and negative predictive value for algorithms relative to medical chart revie

SUBMITTER: Green CA 

PROVIDER: S-EPMC6767606 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature