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Dynamic and Baseline Multi-Task Learning for Predicting Substance Use Initiation in the ABCD Study.

Mengman Wei1, Hanwen Zhang1,2, Qian Peng1

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Summary
This summary is machine-generated.

Dynamic multi-task learning improves adolescent substance use prediction by incorporating longitudinal data. This approach enhances risk factor identification and models temporal changes more effectively than static models.

Keywords:
ABCDLongitudinal AnalysisMulti-Task LearningSubstance Use Initiation

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Area of Science:

  • Adolescent development
  • Neuroscience
  • Public Health

Background:

  • Early substance use initiation is linked to adverse outcomes.
  • Risk factors for substance use are multi-domain and shared across substances.
  • Traditional models struggle with multiple outcomes and complex, non-linear relationships over time.

Purpose of the Study:

  • To develop and evaluate multi-task learning (MTL) frameworks for predicting substance use initiation.
  • To compare static and dynamic MTL models in capturing time-varying risk.
  • To identify common and substance-specific predictors of adolescent substance use.

Main Methods:

  • Utilized the Adolescent Brain Cognitive Development (ABCD) Study data.
  • Developed baseline and dynamic discrete-time MTL models for predicting alcohol, nicotine, and cannabis initiation.
  • Incorporated environmental exposures, covariates, and polygenic risk scores (PRS).

Main Results:

  • MTL models showed comparable or improved prediction performance over single-task logistic regression (LR), especially for low-prevalence substances.
  • Dynamic models incorporating longitudinal data significantly improved prediction accuracy (AUROC gains).
  • Externalizing behavior, parental monitoring, and developmental factors were consistently identified risk factors.

Conclusions:

  • Dynamic multi-task learning effectively predicts adolescent substance use initiation by leveraging longitudinal data and shared information.
  • Time-varying information is crucial for improving prediction performance.
  • Combining baseline and dynamic frameworks offers a robust strategy for understanding adolescent substance use risk.