Funding: ORNL – Low Dose Exposure Evaluation on Human Population Health
Period of Performance: August 18, 2021-July 15, 2023 (ongoing)
Radiation exposure is a known contributor to cancer risk. Unlike high dose exposures, the causal effect of low dose exposure is difficult to quantify because of difficulties in assessing cumulative exposures, long latency periods, and the low excess risk compared to baseline risk. In this ecological analysis, machine learning (ML) will be used to identify associations between county level rates of lung and bronchus cancer incidence and radiation exposure, while controlling for known confounders. Publicly available datasets, modeling and simulations, and artificial intelligence are being used to develop predictive models of (a) typical low-dose radiation exposure within a geographical region and (b) cancer risk as a surrogate for health impact.
