Leveraging natural language processing to identify eligible lung cancer screening patients with the electronic health record.

ABSTRACT:

Objective

To develop and validate an approach that identifies patients eligible for lung cancer screening (LCS) by combining structured and unstructured smoking data from the electronic health record (EHR).

Methods

We identified patients aged 50-80 years who had at least one encounter in a primary care clinic at Vanderbilt University Medical Center (VUMC) between 2019 and 2022. We fine-tuned an existing natural language processing (NLP) tool to extract quantitative smoking information using clinical notes collected from VUMC. Then, we developed an approach to identify patients who are eligible for LCS by combining smoking information from structured data and clinical narratives. We compared this method with two approaches to identify LCS eligibility only using smoking inform

SUBMITTER: Liu S 

PROVIDER: S-EPMC11537206 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature