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Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach

View Article: PubMed Central - PubMed

ABSTRACT

Early childhood anxiety disorders are common, impairing, and predictive of anxiety and mood disorders later in childhood. Epidemiological studies over the last decade find that the prevalence of impairing anxiety disorders in preschool children ranges from 0.3% to 6.5%. Yet, less than 15% of young children with an impairing anxiety disorder receive a mental health evaluation or treatment. One possible reason for the low rate of care for anxious preschoolers is the lack of affordable, timely, reliable and valid tools for identifying young children with clinically significant anxiety. Diagnostic interviews assessing psychopathology in young children require intensive training, take hours to administer and code, and are not available for use outside of research settings. The Preschool Age Psychiatric Assessment (PAPA) is a reliable and valid structured diagnostic parent-report interview for assessing psychopathology, including anxiety disorders, in 2 to 5 year old children. In this paper, we apply machine-learning tools to already collected PAPA data from two large community studies to identify sub-sets of PAPA items that could be developed into an efficient, reliable, and valid screening tool to assess a young child’s risk for an anxiety disorder. Using machine learning, we were able to decrease by an order of magnitude the number of items needed to identify a child who is at risk for an anxiety disorder with an accuracy of over 96% for both generalized anxiety disorder (GAD) and separation anxiety disorder (SAD). Additionally, rather than considering GAD or SAD as discrete/binary entities, we present a continuous risk score representing the child’s risk of meeting criteria for GAD or SAD. Identification of a short question-set that assesses risk for an anxiety disorder could be a first step toward development and validation of a relatively short screening tool feasible for use in pediatric clinics and daycare/preschool settings.

No MeSH data available.


Related in: MedlinePlus

ADTree for Separation Anxiety Disorder.Green boxes represent individual PAPA items; white boxes represent decision points, and blue boxes represent the associated AD-Score.
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pone.0165524.g002: ADTree for Separation Anxiety Disorder.Green boxes represent individual PAPA items; white boxes represent decision points, and blue boxes represent the associated AD-Score.

Mentions: Fig 1 shows the average sensitivity, specificity, and accuracy versus the number of nodes in each tree. For the SAD tree, all three quantities stop improving (and remain stable) after 10 nodes. As depicted in Fig 2, these 10 nodes translate into a minimum of 8 individual PAPA items and a maximum of 17 PAPA items.


Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach
ADTree for Separation Anxiety Disorder.Green boxes represent individual PAPA items; white boxes represent decision points, and blue boxes represent the associated AD-Score.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120781&req=5

pone.0165524.g002: ADTree for Separation Anxiety Disorder.Green boxes represent individual PAPA items; white boxes represent decision points, and blue boxes represent the associated AD-Score.
Mentions: Fig 1 shows the average sensitivity, specificity, and accuracy versus the number of nodes in each tree. For the SAD tree, all three quantities stop improving (and remain stable) after 10 nodes. As depicted in Fig 2, these 10 nodes translate into a minimum of 8 individual PAPA items and a maximum of 17 PAPA items.

View Article: PubMed Central - PubMed

ABSTRACT

Early childhood anxiety disorders are common, impairing, and predictive of anxiety and mood disorders later in childhood. Epidemiological studies over the last decade find that the prevalence of impairing anxiety disorders in preschool children ranges from 0.3% to 6.5%. Yet, less than 15% of young children with an impairing anxiety disorder receive a mental health evaluation or treatment. One possible reason for the low rate of care for anxious preschoolers is the lack of affordable, timely, reliable and valid tools for identifying young children with clinically significant anxiety. Diagnostic interviews assessing psychopathology in young children require intensive training, take hours to administer and code, and are not available for use outside of research settings. The Preschool Age Psychiatric Assessment (PAPA) is a reliable and valid structured diagnostic parent-report interview for assessing psychopathology, including anxiety disorders, in 2 to 5 year old children. In this paper, we apply machine-learning tools to already collected PAPA data from two large community studies to identify sub-sets of PAPA items that could be developed into an efficient, reliable, and valid screening tool to assess a young child’s risk for an anxiety disorder. Using machine learning, we were able to decrease by an order of magnitude the number of items needed to identify a child who is at risk for an anxiety disorder with an accuracy of over 96% for both generalized anxiety disorder (GAD) and separation anxiety disorder (SAD). Additionally, rather than considering GAD or SAD as discrete/binary entities, we present a continuous risk score representing the child’s risk of meeting criteria for GAD or SAD. Identification of a short question-set that assesses risk for an anxiety disorder could be a first step toward development and validation of a relatively short screening tool feasible for use in pediatric clinics and daycare/preschool settings.

No MeSH data available.


Related in: MedlinePlus