Centralized Health and Exposomic Resource (C-HER): Analytic and AI-Ready Data for External Exposomic Research

TL;DR

The C-HER project creates a comprehensive, AI-ready exposomic dataset by integrating diverse environmental data, enabling advanced health research and disease prediction through machine learning.

Contribution

It develops a novel data engineering approach to link complex spatio-temporal exposomic data, facilitating AI applications in health research.

Findings

Over 30 exposomic datasets integrated into AAIRD

Enables modeling of air pollution and regional environmental indicators

Supports future disease prediction studies using ML and deep learning

Abstract

The Centralized Health and Exposomic Resource (C-HER) project has identified, profiled, spatially indexed, and stored over 30 external exposomic datasets. The resulting analytic and AI-ready data (AAIRD) provides a significant opportunity to develop an integrated picture of the exposome for health research. The exposome is a conceptual framework designed to guide the study of the complex environmental and genetic factors that together shape human health. Few composite measures of the exposome exist due to the high dimensionality of exposure data, multimodal data sources, and varying spatiotemporal scales. We develop a data engineering solution that overcomes the challenges of spatio-temporal linkage in this field. We provide examples of how environmental data can be combined to characterize a region, model air pollution, or provide indicators for cancer research. The development of AAIRD will allow future studies to use ML and deep learning methods to generate spatial and contextual exposure data for disease prediction.