Predicting early-onset COPD risk in adults aged 20-50 using electronic health records and machine learning.

ABSTRACT: Chronic obstructive pulmonary disease (COPD) is a major public health concern, affecting estimated 164 million people worldwide. Early detection and intervention strategies are essential to reduce the burden of COPD, but current screening approaches are limited in their ability to accurately predict risk. Machine learning (ML) models offer promise for improved accuracy of COPD risk prediction by combining genetic and electronic medical record data. In this study, we developed and evaluated eight ML models for primary screening of COPD utilizing routine screening data, polygenic risk scores (PRS), additional clinical data, or a combination of all three. To assess our models, we conducted a retrospective analysis of approximately 329,396 patients in the UK Biobank database. Incorporating per

SUBMITTER: Liu G 

PROVIDER: S-EPMC10896079 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature