Quantitative modeling of mortality patterns in dogs exposed to alpha particle emitting radionuclides: Insights from competing risks and causal inference machine learning.

ABSTRACT: This study employed state-of-the-art machine learning to evaluate the mortality effects of alpha-emitting radionuclides (241Am, 249Cf, 252Cf, 238Pu, 239Pu, 224Ra, 226Ra, 228Th) on 2,576 dogs, factoring in radioactivity levels, composition, administration method (injection or inhalation), and age at exposure. There were 972 cancer deaths, 599 non-cancer deaths, 789 deaths from many diseases (involving several diagnoses, including both cancer and non-cancer pathologies), and 216 deaths with uncertain causes. A Random Survival Forest model for overall mortality achieved concordance scores of 0.763 and 0.745 on training and testing data subsets, respectively. A model variant with competing risks was used to investigate mortality trends over time for different disease categories. It achieved co

SUBMITTER: Wang E 

PROVIDER: S-EPMC12279112 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature