Crippa, A;
Salvatore, C;
Molteni, E;
Mauri, M;
Salandi, A;
Trabattoni, S;
Agostoni, C;
... Castiglioni, I; + view all
(2017)
The Utility of a Computerized Algorithm Based on a Multi-Domain Profile of Measures for the Diagnosis of Attention Deficit/Hyperactivity Disorder.
Front. Psychiatry
, 8
, Article 189. 10.3389/fpsyt.2017.00189.
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Abstract
The current gold standard for diagnosis of attention deficit/hyperactivity disorder (ADHD) includes subjective measures, such as clinical interview, observation, and rating scales. The significant heterogeneity of ADHD symptoms represents a challenge for this assessment and could prevent an accurate diagnosis. The aim of this work was to investigate the ability of a multi-domain profile of measures, including blood fatty acid (FA) profiles, neuropsychological measures, and functional measures from near-infrared spectroscopy (fNIRS), to correctly recognize school-aged children with ADHD. To answer this question, we elaborated a supervised machine-learning method to accurately discriminate 22 children with ADHD from 22 children with typical development by means of the proposed profile of measures. To assess the performance of our classifier, we adopted a nested 10-fold cross validation, where the original dataset was split into 10 subsets of equal size, which were used repeatedly for training and testing. Each subset was used once for performance validation. Our method reached a maximum diagnostic accuracy of 81% through the combining of the predictive models trained on neuropsychological, FA profiles, and deoxygenated-hemoglobin features. With respect to the analysis of a single-domain dataset per time, the most discriminant neuropsychological features were measures of vigilance, focused and sustained attention, and cognitive flexibility; the most discriminating blood FAs were linoleic acid and the total amount of polyunsaturated fatty acids. Finally, with respect to the fNIRS data, we found a significant advantage of the deoxygenated-hemoglobin over the oxygenated-hemoglobin data in terms of predictive accuracy. These preliminary findings show the feasibility and applicability of our machine-learning method in correctly identifying children with ADHD based on multi-domain data. The present machine-learning classification approach might be helpful for supporting the clinical practice of diagnosing ADHD, even fostering a computer-aided diagnosis perspective.
| Type: | Article |
|---|---|
| Title: | The Utility of a Computerized Algorithm Based on a Multi-Domain Profile of Measures for the Diagnosis of Attention Deficit/Hyperactivity Disorder |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fpsyt.2017.00189 |
| Publisher version: | https://doi.org/10.3389/fpsyt.2017.00189 |
| Language: | English |
| Additional information: | © 2017 Crippa, Salvatore, Molteni, Mauri, Salandi, Trabattoni, Agostoni, Molteni, Nobile and Castiglioni. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Psychiatry, Attention Deficit/hyperactivity Disorder, Machine Learning, Support Vector Machines, Near-Infrared Spectroscopy, Fatty Acids, Near-Infrared Spectroscopy, Adhd, Children, Deficit, Fnirs, Classification, Methodology, Inhibition, Validity, Subtypes |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10026079 |
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