AI-driven chemotoxicity prediction in colorectal cancer: impact of race, SDOH, and biological aging.

ABSTRACT:

Background

Patients with colorectal cancer (CRC) often experience chemotoxicity that impacts treatment adherence, survival, and quality of life. Early screening for chemotoxicity risk is vital, yet comprehensive predictive models are lacking. The objective of this study was to develop effective artificial intelligence (AI)/machine learning (ML) models, integrating racialized group, social determinants of health (SDOH) (Area Deprivation Index [ADI], employment status), and biological aging (Levine Phenotypic Age) to predict overall, gastrointestinal (GI), and hematological chemotoxicity.

Methods

We used electronic health records data from 1,735 adult patients with CRC. Sociodemographic/clinical variables, Levine Phenotypic Age (biological aging), and SDOH (including geospatia

SUBMITTER: Han C 

PROVIDER: S-EPMC12502520 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature