Jesse C Bledsoe1,2, Cao Xiao3, Art Chaovalitwongse3,4, Sonya Mehta4, Thomas J Grabowski4,5, Margaret Semrud-Clikeman6, Steven Pliszka7, David Breiger1,2. 1. University of Washington School of Medicine, Seattle, USA. 2. Seattle Children's Hospital, Seattle, USA. 3. University of Washington, Department of Industrial and Systems Engineering, Seattle, USA. 4. University of Washington, Department of Radiology, Seattle, USA. 5. University of Washington, Department of Neurology, Seattle, USA. 6. University of Minnesota Medical School, Minneapolis, USA. 7. University of Texas Health and Science Center, San Antonio, USA.
Abstract
Objective: Common methods for clinical diagnosis include clinical interview, behavioral questionnaires, and neuropsychological assessment. These methods rely on clinical interpretation and have variable reliability, sensitivity, and specificity. The goal of this study was to evaluate the utility of machine learning in the prediction and classification of children with ADHD-Combined presentation (ADHD-C) using brief neuropsychological measures (d2 Test of Attention, Children with ADHD-C and typically developing control children completed semi-structured clinical interviews and measures of attention/concentration and parents completed symptom severity questionnaires. Method: We used a forward feature selection method to identify the most informative neuropsychological features for support vector machine (SVM) classification and a decision tree model to derive a rule-based model. Results: The SVM model yielded excellent classification accuracy (100%) of individual children with and without ADHD (1.0). Decision tree algorithms identified individuals with and without ADHD-C with 100% sensitivity and specificity. Conclusion: This study observed highly accurate statistical diagnostic classification, at the individual level, in a sample of children with ADHD-C. The findings suggest data-driven behavioral algorithms based on brief neuropsychological data may present an efficient and accurate diagnostic tool for clinicians.
Objective: Common methods for clinical diagnosis include clinical interview, behavioral questionnaires, and neuropsychological assessment. These methods rely on clinical interpretation and have variable reliability, sensitivity, and specificity. The goal of this study was to evaluate the utility of machine learning in the prediction and classification of children with ADHD-Combined presentation (ADHD-C) using brief neuropsychological measures (d2 Test of Attention, Children with ADHD-C and typically developing control children completed semi-structured clinical interviews and measures of attention/concentration and parents completed symptom severity questionnaires. Method: We used a forward feature selection method to identify the most informative neuropsychological features for support vector machine (SVM) classification and a decision tree model to derive a rule-based model. Results: The SVM model yielded excellent classification accuracy (100%) of individual children with and without ADHD (1.0). Decision tree algorithms identified individuals with and without ADHD-C with 100% sensitivity and specificity. Conclusion: This study observed highly accurate statistical diagnostic classification, at the individual level, in a sample of children with ADHD-C. The findings suggest data-driven behavioral algorithms based on brief neuropsychological data may present an efficient and accurate diagnostic tool for clinicians.
Authors: Alessandro Crippa; Christian Salvatore; Erika Molteni; Maddalena Mauri; Antonio Salandi; Sara Trabattoni; Carlo Agostoni; Massimo Molteni; Maria Nobile; Isabella Castiglioni Journal: Front Psychiatry Date: 2017-10-03 Impact factor: 4.157
Authors: María Vallejo-Valdivielso; Pilar de Castro-Manglano; Azucena Díez-Suárez; Juan J Marín-Méndez; Cesar A Soutullo Journal: Clin Pract Epidemiol Ment Health Date: 2019-12-31