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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Predicting adolescent psychopathology from early life factors: A machine learning tutorial.

Faizaan Siddique1,2, Brian K Lee1,3

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Drexel University, Philadelphia, PA, United States of America.

Global Epidemiology
|September 16, 2024
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Summary

Machine learning models can predict adolescent psychopathology using early life factors like family history and sociodemographics. These models offer moderate accuracy for risk prediction in youth.

Keywords:
AdolescentChildMachine learningMental disordersPregnancyRisk prediction

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Area of Science:

  • Epidemiology
  • Machine Learning
  • Adolescent Health

Background:

  • Machine learning (ML) implementation in epidemiology requires programming expertise.
  • Predicting adolescent psychopathology using early life factors is crucial for early intervention.

Purpose of the Study:

  • To demonstrate ML for risk prediction of adolescent psychopathology.
  • To assess the utility of early life factors (prenatal, family history, sociodemographic) in predicting psychopathology.

Main Methods:

  • Utilized data from 9643 adolescents (ages 9-10) from the Adolescent Brain and Cognitive Development (ABCD) Study.
  • Employed 5 ML algorithms to predict high Child Behavior Checklist (CBCL) scores.
  • Evaluated model performance using sensitivity, specificity, F1-score, and AUC.

Main Results:

  • Elastic net and gradient boosted trees showed the best performance.
  • Models including prenatal and family history factors achieved AUC of 0.742-0.745.
  • Family history and sociodemographic factors were strong predictors of adolescent psychopathology.

Conclusions:

  • ML models incorporating prenatal, family history, and sociodemographic factors can moderately predict adolescent psychopathology.
  • Considerations for model overfitting and hyperparameter tuning are essential.
  • Future models may benefit from additional relevant covariates for improved prediction accuracy.