Predicting individual cases of major adolescent psychiatric conditions with artificial intelligence

Abstract

Three-quarters of lifetime mental illness occurs by the age of 24, but relatively little is known about how to robustly identify youth at risk to target intervention efforts known to improve outcomes. Barriers to knowledge have included obtaining robust predictions while simultaneously analyzing large numbers of different types of candidate predictors. In a new, large, transdiagnostic youth sample and multidomain high-dimension data, we used 160 candidate predictors encompassing neural, prenatal, developmental, physiologic, sociocultural, environmental, emotional and cognitive features and leveraged three different machine learning algorithms optimized with a novel artificial intelligence meta-learning technique to predict individual cases of anxiety, depression, attention deficit, disruptive behaviors and post-traumatic stress. Our models tested well in unseen, held-out data (AUC ≥ 0.94). By utilizing a large-scale design and advanced computational approaches, we were able to compare the relative predictive ability of neural versus psychosocial features in a principled manner and found that psychosocial features consistently outperformed neural metrics in their relative ability to deliver robust predictions of individual cases. We found that deep learning with artificial neural networks and tree-based learning with XGBoost outperformed logistic regression with ElasticNet, supporting the conceptualization of mental illnesses as multifactorial disease processes with non-linear relationships among predictors that can be robustly modeled with computational psychiatry techniques. To our knowledge, this is the first study to test the relative predictive ability of these gold-standard algorithms from different classes across multiple mental health conditions in youth within the same study design in multidomain data utilizing >100 candidate predictors. Further research is suggested to explore these findings in longitudinal data and validate results in an external dataset.

The Power Threat Meaning Framework: a qualitative study of depression in adolescents and young adults

“…In this context the PTMF may be helpful in eliciting adverse experiences that were previously obscured. As there is a growing body of evidence that suggests that difficult lives explain depression better than broken brains ( 61 , 62 ), treatment approaches that acknowledge this can potentially be more effective than interventions that primarily deal with downstream symptoms. One implication of our findings on the relevance of negative operations of power in the context of depression is that intersectional analyses of these mechanisms may be useful, both to enable individual empowerment, restored self-confidence and better life navigation skills as part of depression treatment.…”

Prediction of Junior High School Students’ Problematic Internet Use: The Comparison of Neural Network Models and Linear Mixed Models in Longitudinal Study

“…The results showed that both neural network model and decision tree model have higher predictability and are better than linear regression model. 7 …”

“…The results showed that both neural network model and decision tree model have higher predictability and are better than linear regression model. 7 However, the variables studied in the medical field are more objective measurements, while in the field of psychology, they are more subjective variables. In addition, in the field of psychology, such as junior high school students' questionable Internet use and adolescent mental health problems, 8 it is easy to be affected by many variables, such as individual characteristics, school, family, environment and so on.…”