Natural Language Processing and Machine Learning to Identify People Who Inject Drugs in Electronic Health Records

ABSTRACT: Abstract

Background

Improving the identification of people who inject drugs (PWID) in electronic medical records can improve clinical decision making, risk assessment and mitigation, and health service research. Identification of PWID currently consists of heterogeneous, nonspecific International Classification of Diseases (ICD) codes as proxies. Natural language processing (NLP) and machine learning (ML) methods may have better diagnostic metrics than nonspecific ICD codes for identifying PWID.

Methods

We manually reviewed 1000 records of patients diagnosed with Staphylococcus aureus bacteremia admitted to Veterans Health Administration hospitals from 2003 through 2014. The manual review was the reference standard. We developed and trained NLP/ML algorithms with and with

SUBMITTER:  

PROVIDER: S-EPMC9511274 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature