Machine learning predicts conduct disorder in kids
Highly accurate model uses factors across biopsychosocial domains

Date:
April 12, 2022
Source:
Elsevier
Summary:
Conduct disorder (CD) is a common yet complex psychiatric disorder
featuring aggressive and destructive behavior. Factors contributing
to the development of CD span biological, psychological, and
social domains.

Researchers have identified a myriad of risk factors that could
help predict CD, but they are often considered in isolation. Now,
a new study uses a machine-learning approach for the first time
to assess risk factors across all three domains in combination
and predict later development of CD with high accuracy.



FULL STORY ========================================================================== Conduct disorder (CD) is a common yet complex psychiatric disorder
featuring aggressive and destructive behavior. Factors contributing
to the development of CD span biological, psychological, and social
domains. Researchers have identified a myriad of risk factors that
could help predict CD, but they are often considered in isolation. Now,
a new study uses a machine-learning approach for the first time to assess
risk factors across all three domains in combination and predict later development of CD with high accuracy.


==========================================================================
The study appears in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, published by Elsevier.

The 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 model correctly predicted the development of CD two years
later with over 90% accuracy.

Cameron Carter, MD, Editor of Biological Psychiatry: Cognitive
Neuroscience and Neuroimaging, said of the study: "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 behavior than on the mother's mood per se." 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.

"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," said senior author Arielle Baskin-Sommers, 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."

========================================================================== Story Source: Materials provided by Elsevier. Note: Content may be edited
for style and length.


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

Link to news story: https://www.sciencedaily.com/releases/2022/04/220412095330.htm

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