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Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions
BACKGROUND: Real-time detection of drinking could improve timely delivery of interventions aimed at reducing alcohol consumption and alcohol-related injury, but existing detection methods are burdensome or impractical. OBJECTIVE: To evaluate whether phone sensor data and machine learning models are...
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| Publicado no: | Addict Behav |
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| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2017
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5963979/ https://ncbi.nlm.nih.gov/pubmed/29217132 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.addbeh.2017.11.039 |
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