Utility of machine learning in developing a predictive model for early-age-onset colorectal neoplasia using electronic health records.

ABSTRACT:

Background and aims

The incidence of colorectal cancer (CRC) is increasing in adults younger than 50, and early screening remains challenging due to cost and under-utilization. To identify individuals aged 35-50 years who may benefit from early screening, we developed a prediction model using machine learning and electronic health record (EHR)-derived factors.

Methods

We enrolled 3,116 adults aged 35-50 at average-risk for CRC and underwent colonoscopy between 2017-2020 at a single center. Prediction outcomes were (1) CRC and (2) CRC or high-risk polyps. We derived our predictors from EHRs (e.g., demographics, obesity, laboratory values, medications, and zip code-derived factors). We constructed four machine learning-based models using a training set (random sample of 70% of

SUBMITTER: Hussan H 

PROVIDER: S-EPMC9064446 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature