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Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use.

Chrianna BharatMeyer D GlantzSergio Aguilar-GaxiolaJordi AlonsoRonny BruffaertsBrendan BuntingJosé Miguel Caldas-de-AlmeidaGraça CardosoStephanie ChardoulPeter de JongeOye GurejeJosep Maria HaroMeredith G HarrisElie G KaramNorito KawakamiAndrzej KiejnaViviane Kovess-MasfetySing LeeJohn G McGrathJacek MoskalewiczFernando Navarro-MateuCharlene RapseyNancy A SampsonKate M ScottHisateru TachimoriMargreet Ten HaveGemma VilagutBogdan WojtyniakMiguel XavierRonald C KesslerLouisa Degenhardt
Published in: Addiction (Abingdon, England) (2023)
A risk algorithm can be created using data collected at the onset of regular alcohol use to target youth at highest risk of alcohol dependence by early adulthood. Important considerations remain for advancing the development and practical implementation of such models.
Keyphrases
  • early onset
  • machine learning
  • late onset
  • deep learning
  • healthcare
  • primary care
  • depressive symptoms
  • physical activity
  • mental health
  • young adults
  • big data
  • neural network
  • early life