Machine Learning and Real-World Data to Predict Lung Cancer Risk in Routine Care.

ABSTRACT:

Background

This study used machine learning to develop a 3-year lung cancer risk prediction model with large real-world data in a mostly younger population.

Methods

Over 4.7 million individuals, aged 45 to 65 years with no history of any cancer or lung cancer screening, diagnostic, or treatment procedures, with an outpatient visit in 2013 were identified in Optum's de-identified Electronic Health Record (EHR) dataset. A least absolute shrinkage and selection operator model was fit using all available data in the 365 days prior. Temporal validation was assessed with recent data. External validation was assessed with data from Mercy Health Systems EHR and Optum's de-identified Clinformatics Data Mart Database. Racial inequities in model discrimination were assessed with xAUCs.

SUBMITTER: Chandran U 

PROVIDER: S-EPMC9986687 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature