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A comparison of machine learning algorithms for the surveillance of autism spectrum disorder
OBJECTIVE: The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive process to measure the prevalence of autism spectrum disorder (ASD) among children in the United States. Random forests methods have shown promise in speeding up this process, but they lag behind human clas...
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| I publikationen: | PLoS One |
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| Huvudupphovsmän: | , , |
| Materialtyp: | Artigo |
| Språk: | Inglês |
| Publicerad: |
Public Library of Science
2019
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| Ämnen: | |
| Länkar: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6760799/ https://ncbi.nlm.nih.gov/pubmed/31553774 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0222907 |
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