A research study, that recently appeared in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, published by the journal Elsevier, demonstrated that conduct disorder in children can be predicted by machine learning technology.
What is conduct disorder?
Conduct disorder is a common yet complex psychiatric disorder featuring aggressive and destructive behaviour. Factors contributing to the development of conduct disorder in children span biological, psychological, and social domains.
Researchers have identified a myriad of risk factors that could help predict conduct disorder in children, but they are often considered in isolation. Now, this novel study utilises a machine-learning approach for the first time to assess risk factors across all three domains in combination and to predict later development of conduct disorder with greater accuracy.
Machine learning: How was it utilised to predict conduct disorder in children?
Scientists employed baseline data from over 2,300 children aged nine to 10 that were enrolled in the Adolescent Brain Cognitive Development (ABCD) Study – a longitudinal study following the biopsychosocial development of children.
The researchers ‘trained’ their machine-learning model utilising previously identified risk factors from across multiple biopsychosocial domains. For example, measures included brain imaging (biological), cognitive abilities (psychological), and familial characteristics (social). The model correctly predicted the development of conduct disorder in children two years later with over 90% accuracy.
What could this technology mean for the future of diagnosing this disorder?
Cameron Carter, MD, Editor of Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, noted: “These striking results using task-based functional MRI to investigate the function of the reward system suggest that risk for later depression in children of depressed mothers may depend more on mothers’ responses to their children’s emotional behaviour than on the mother’s mood per se.”
The ability to accurately predict potential conduct disorder in children would aid researchers and healthcare workers in designing interventions for at-risk youth with the potential to minimise or even prevent the harmful effects of conduct disorder on both children and their families.
“Findings from our study highlight the added value of combining neural, social, and psychological factors to predict conduct disorder, a burdensome psychiatric problem in youth,” concluded Arielle Baskin-Sommers, Senior Author, and PhD at Yale University, New Haven, CT, USA.
“These findings offer promise for developing more precise identification and intervention approaches that consider the multiple factors that contribute to this disorder. They also highlight the utility of leveraging large, open-access datasets, such as ABCD, that collect measures about the individual across levels of analysis.”
