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Related Concept Videos

Oppositional Defiant Disorder01:30

Oppositional Defiant Disorder

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A persistent pattern of angry or irritable mood, defiant behavior, or vindictiveness characterizes Oppositional Defiant Disorder (ODD). Symptoms must occur over at least six months, involve interactions with individuals beyond siblings, and meet specific diagnostic criteria to be clinically significant. The disorder affects emotional regulation, social interactions, and behavior, often manifesting early in life and influencing long-term development and functioning.
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Antisocial personality disorder is a chronic mental health condition characterized by persistent patterns of disregard for the rights and well-being of others. Individuals with antisocial personality disorder exhibit behaviors that include deceitfulness, impulsivity, irresponsibility, aggression, and a profound lack of empathy. These traits often manifest early in life and persist into adulthood, leading to significant personal, social, and legal consequences.
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Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
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Machine learning-based prediction of persistent oppositional defiant behavior for 5 years.

Kyoung-Sae Na1, Zong Woo Geem2, Seo-Eun Cho1

  • 1Department of Psychiatry, Gachon University College of Medicine, Gil Medical Center, Incheon, Republic of Korea.

Nordic Journal of Psychiatry
|July 22, 2020
PubMed
Summary
This summary is machine-generated.

Machine learning effectively predicts persistent oppositional defiant behavior (ODB) in young children. This early detection aids timely intervention for at-risk youth, improving outcomes.

Keywords:
Machine learningchildrenexternalizing behavioroppositional defiant disorderrandom forest

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Area of Science:

  • Child Psychology
  • Machine Learning Applications
  • Behavioral Science

Background:

  • Early identification of oppositional defiant behavior (ODB) is crucial for timely intervention in at-risk children.
  • Persistent ODB can have long-term negative impacts on a child's development and well-being.

Purpose of the Study:

  • To develop and validate a predictive model for persistent oppositional defiant behavior using a machine learning algorithm.
  • To assess the accuracy and effectiveness of the predictive model in identifying children with persistent ODB.

Main Methods:

  • A random forest, a tree-based ensemble model, was employed to construct the predictive model.
  • Nationwide cohort data from 2012 to 2017, including 1,323 preschool-aged children, was utilized.
  • Performance was evaluated using metrics such as Area Under the Curve (AUC), accuracy, sensitivity, specificity, and Matthew's Correlation Coefficient (MCC).

Main Results:

  • The machine learning model achieved high predictive performance on the hold-out test set.
  • Key performance metrics included an overall accuracy of 0.955, AUC of 0.982, sensitivity of 1.000, specificity of 0.954, and MCC of 0.417.
  • The prevalence of persistent oppositional defiant behavior in the cohort was 0.98%.

Conclusions:

  • The study confirms the utility of a machine learning-based approach for predicting persistent oppositional defiant behavior in preschool-aged children.
  • This predictive model offers a promising tool for early detection and targeted interventions.
  • Further research can explore refining the model and its application in clinical settings.