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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.


Example Clinical Screening Form.Example of an auto-scoring form populated with questions and AD-Scores from the GAD ADTree depicted in Fig 3. As the examiner answers the questions (denoted as an “X” next to the associated answer in the “Response” column), the score sheet automatically assigns the associated AD-Score and calculates a cumulative risk score using the equation: Risk Score = 1 − (1/(exp(AD − Score) + 1)) with each additional question.
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pone.0165524.g005: Example Clinical Screening Form.Example of an auto-scoring form populated with questions and AD-Scores from the GAD ADTree depicted in Fig 3. As the examiner answers the questions (denoted as an “X” next to the associated answer in the “Response” column), the score sheet automatically assigns the associated AD-Score and calculates a cumulative risk score using the equation: Risk Score = 1 − (1/(exp(AD − Score) + 1)) with each additional question.

Mentions: Fig 4 shows the relationship between the risk score and the probability that the child met criteria for GAD (Fig 4A) or SAD (Fig 4B) on the full PAPA interview in the PTRTS test sample. As the risk score increases towards 1, the probability (weighted to the screening sample) that the child will meet criteria for the disorder approaches 100%. Using this risk score, the information included in the ADTrees, and the associated AD-scores, can be translated into a clinically instructive form. Fig 5 illustrates for the GAD ADTree how this approach can be translated into a clinical form that automatically calculates the cumulative risk for the GAD as one proceeds through the hierarchy of the ADTree items. This type of clinical tool will need to be tested for reliability, validity, and acceptability in a future clinical study. An equivalent form could be made from the SAD ADTree.


Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach
Example Clinical Screening Form.Example of an auto-scoring form populated with questions and AD-Scores from the GAD ADTree depicted in Fig 3. As the examiner answers the questions (denoted as an “X” next to the associated answer in the “Response” column), the score sheet automatically assigns the associated AD-Score and calculates a cumulative risk score using the equation: Risk Score = 1 − (1/(exp(AD − Score) + 1)) with each additional question.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0165524.g005: Example Clinical Screening Form.Example of an auto-scoring form populated with questions and AD-Scores from the GAD ADTree depicted in Fig 3. As the examiner answers the questions (denoted as an “X” next to the associated answer in the “Response” column), the score sheet automatically assigns the associated AD-Score and calculates a cumulative risk score using the equation: Risk Score = 1 − (1/(exp(AD − Score) + 1)) with each additional question.
Mentions: Fig 4 shows the relationship between the risk score and the probability that the child met criteria for GAD (Fig 4A) or SAD (Fig 4B) on the full PAPA interview in the PTRTS test sample. As the risk score increases towards 1, the probability (weighted to the screening sample) that the child will meet criteria for the disorder approaches 100%. Using this risk score, the information included in the ADTrees, and the associated AD-scores, can be translated into a clinically instructive form. Fig 5 illustrates for the GAD ADTree how this approach can be translated into a clinical form that automatically calculates the cumulative risk for the GAD as one proceeds through the hierarchy of the ADTree items. This type of clinical tool will need to be tested for reliability, validity, and acceptability in a future clinical study. An equivalent form could be made from the SAD ADTree.

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.