Can Conduct Disorder be Accurately Predicted?

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Can Conduct Disorder be Accurately Predicted?   

Jim Windell

           Conduct disorder is a type of behavior disorder and it is seen in adolescents when they have severe antisocial behavior. Typically, according to John Hopkins Medicine, youth diagnosed with conduct disorder (CD) show a disregard for basic social standards and rules. And beyond that, usually they may physically assault or harm others.

           Experts believe that many factors play a role in conduct disorder. These factors include brain damage, a traumatic event, genes, child abuse and social problems. Previous researchers have identified a myriad of risk factors that could help predict CD, but these risk factors are often considered in isolation.

           Now, though, a new study, recently published in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, used a machine-learning approach for the first time to assess risk factors across three domains – biological, psychological, and social – in combination to try to predict the later development of CD.

           Machine learning is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. In effect, machine learning is the science of getting computers to act without being explicitly programmed. Algorithms are used in machine learning to process historical data to predict new output values.

           In this new study, researchers used baseline data from over 2,300 children aged 9 to 10 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 using previously identified risk factors from across multiple biopsychosocial domains. For example, measures included brain imaging (biological), cognitive abilities (psychological), and family characteristics (social). The outcome was that the model correctly predicted the development of CD two years later with over 90% accuracy.

           According to senior author Arielle Baskin-Sommers, Ph.D, at Yale University, “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.” Baskins-Sommers went on to say that: “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.”

           The ability to accurately predict who might develop CD would aid researchers and healthcare workers in designing interventions for at-risk youth with the potential to minimize or even prevent the harmful effects of CD on children and their families – and even communities.

           To read this article, finds it with this reference:

Lena Chan, Cortney Simmons, Scott Tillem, May Conley, Inti A. Brazil, Arielle Baskin-Sommers. (2022). Classifying Conduct Disorder using a biopsychosocial model and machine learning method. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging; DOI: 10.1016/j.bpsc.2022.02.004


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