Machine learning prediction of exposure to acrylamide based on modelling of association between dietary exposure and internal biomarkers
Wan X, Zhang Y, Gao S, Shen X, Jia W, Pan X, Zhuang P, Jiao J, Zhang Y. Machine learning prediction of exposure to acrylamide based on modelling of association between dietary exposure and internal biomarkers. Food And Chemical Toxicology 2022, 170: 113498. PMID: 36328216, DOI: 10.1016/j.fct.2022.113498.Peer-Reviewed Original ResearchMeSH KeywordsAcetylcysteineAcrylamideAgedBiomarkersDietary ExposureHumansMachine LearningMiddle AgedConceptsDietary exposureElderly populationInternal exposureTotal energy intakeDietary acrylamide exposureChinese elderly populationAverage dietary intakeN-acetylExposure assessmentRegression modelsUrinary biomarkersDietary intakeUrinary contentAcrylamide exposureChinese cohortPhysical activityAccurate exposure assessmentEnergy intakeElderly participantsPotential health risksL-cysteineImportant covariatesLinear regression modelsHealth risksExposure