A comparison of machine learning algorithms for the surveillance of autism spectrum disorder - Lee, Scott H.; Maenner, Matthew J.; Heilig, Charles M. (PloS one, 2019)
Keywords: Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; Autism; Autism Spectrum Disorder - diagnosis; Autism Spectrum Disorder - epidemiology; Bayesian analysis; Biology and Life Sciences; Care and treatment; Centers for Disease Control and Prevention, U.S; Child; Children; Children & youth; Classification; Computer and Information Sciences; Deep Learning; Developmental disabilities; Disease control; Disease prevention; Epidemiological Monitoring; Feasibility Studies; Forests; Georgia; Humans; International conferences; Learning algorithms; Linguistics; Machine learning; Medicine and Health Sciences; Neural networks; People and Places; Pervasive developmental disorders; Physical Sciences; Prevalence; Public Health Surveillance - methods; Research and Analysis Methods; Risk factors; Sentiment analysis; Social Sciences; Support Vector Machine; Support vector machines; Surveillance; Surveillance systems; United States - epidemiology; Workflow; United States; Georgia; Atlanta Georgia; United States--US