Supporting diagnosis of attention-deficit hyperactive disorder with novelty detection

Artificial Intelligence in Medicine
Hyoung-Joo LeeMin-Sup Shin

Abstract

Computerized continuous performance test (CPT) is a widely used diagnostic tool for attention-deficit hyperactivity disorder (ADHD). It measures the number of correctly detected stimuli as well as response times. Typically, when calculating a cut-off score for discriminating between normal and abnormal, only the normal children's data are collected. Then the average and standard deviation of each measure or variable is computed. If any of variables is larger than 2 sigma above the average, that child is diagnosed as abnormal. We will call this approach as "T-score 70" classifier. However, its performance has a lot to be desired due to a high false negative error. In order to improve the classification accuracy we propose to use novelty detection approaches for supporting ADHD diagnosis. Novelty detection is a model building framework where a classifier is constructed using only one class of training data and a new input pattern is classified according to its similarity to the training data. A total of eight novelty detectors are introduced and applied to our ADHD datasets collected from two modes of tests, visual and auditory. They are evaluated and compared with the T-score model on validation datasets in terms of false positi...Continue Reading

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Citations

Jul 15, 2016·Journal of Attention Disorders·Jangho ParkHyo-Won Kim
Feb 6, 2009·Journal of Developmental and Behavioral Pediatrics : JDBP·Yehuda PollakVarda Gross-Tsur
Sep 17, 2018·European Child & Adolescent Psychiatry·Kristin Romvig OvergaardCarsten Obel

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