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2 results

  • Application of the decision tree model in ADHD screening

  • L.R. Carreiro, M. Silva, M.C. Teixeira
  • Journal:
    European Psychiatry / Volume 64 / Issue S1 / April 2021
    Published online by Cambridge University Press:
    13 August 2021, pp. S754-S755
    • Article
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      • Open access
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  • Introduction

    Attention Deficit Hyperactivity Disorder (ADHD) is a Neurodevelopmental Disorder characterized by persistent pattern of inattention and hyperactivity / impulsivity. There is considerable difficulty in diagnosing ADHD, mainly to discriminate what could be symptoms arising from ADHD or typical age behaviors. The decision tree model is a statistical algorithm, a predictive model built with comparisons of values for a given objective that can be compared with other constant values, placing these variables in a database at hierarchical levels.

    Objectives

    This study aims to apply the decision tree model in directing the screening of ADHD complaints to analyze which cognitive and behavioral parameters would be better associations with ADHD accurate diagnosis

    Methods

    We used a database of research protocol with 202 children assessed with complaints of ADHD and a control group with 185 participants. Decision tree analyzed parameters selected from the cognitive instruments, such voluntary attention, Continuous Performance Test indexes, WCST indexes, Wechsler Intelligence indexes and behavioral scales from CBCL/6-1 and TRF/6-18.

    Results

    The highlighted results points to WCST index like: “Perseverative answers” and “Perseverative errors” and “learning to learn” joint to “CPT omissions” and behavioral scales as “CBCL ADHD”, and “CBCL Problems of Attention” produces accuracy of diagnosis discrimination from 84.7% to 60% in the precision of the decision tree.

    Conclusions

    The decision tree and machine learning approaches can be effective in directing the screening of typical ADHD complaints.

    Disclosure

    No significant relationships.

  • 37 - Neuroimaging of Williams–Beuren syndrome

  • from Section VII - Developmental Disorders
    • By Andreia Santos, Central Institute of Mental Health Mannheim, Germany, Andreas Meyer-Lindenberg, Department of Psychiatry and Psychotherapy University of Heidelberg and Central Institute of Mental Health Mannheim, Germany
  • Edited by Martha E. Shenton, Bruce I. Turetsky, University of Pennsylvania
  • Book:
    Understanding Neuropsychiatric Disorders
    Published online:
    10 January 2011
    Print publication:
    09 December 2010, pp 537-554

  • Summary

    This chapter reviews imaging studies delineating the unique neuropsychiatric features of Williams-Beuren syndrome (WS), as well as recent advances in investigating the neural substrates of the disorder, which have provided significant contributions to unraveling the impact of a specific genetic defect on brain structure and function. It discusses the clinical, behavioral, cognitive and genetic profiles of WS. Studies using high-resolution magnetic resonance imaging (MRI) have found significant brain differences between WS and typically developing individuals. Significant advances in the understanding of the structural basis of WS have come from the application of voxel-based morphometry (VBM), which allows the study of genetic variation without restriction to anatomical boundaries. Findings of the studies reviewed in the chapter offer a systems-level characterization of genetically mediated abnormalities of neural interactions that can be probed for the identification of single-gene effects on brain maturation.