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Exploring telediagnostic procedures in child neuropsychiatry: addressing ADHD diagnosis and autism symptoms through supervised machine learning

Recently, there has been an increase in telemedicine applied to child neuropsychiatry, such as the use of online platforms to collect remotely case histories and demographic and behavioral information. In the present proof-of-concept study, we aimed to understand to what extent information parents a...

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Autores principales: Grazioli, Silvia, Crippa, Alessandro, Rosi, Eleonora, Candelieri, Antonio, Ceccarelli, Silvia Busti, Mauri, Maddalena, Manzoni, Martina, Mauri, Valentina, Trabattoni, Sara, Molteni, Massimo, Colombo, Paola, Nobile, Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875192/
https://www.ncbi.nlm.nih.gov/pubmed/36695897
http://dx.doi.org/10.1007/s00787-023-02145-4
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author Grazioli, Silvia
Crippa, Alessandro
Rosi, Eleonora
Candelieri, Antonio
Ceccarelli, Silvia Busti
Mauri, Maddalena
Manzoni, Martina
Mauri, Valentina
Trabattoni, Sara
Molteni, Massimo
Colombo, Paola
Nobile, Maria
author_facet Grazioli, Silvia
Crippa, Alessandro
Rosi, Eleonora
Candelieri, Antonio
Ceccarelli, Silvia Busti
Mauri, Maddalena
Manzoni, Martina
Mauri, Valentina
Trabattoni, Sara
Molteni, Massimo
Colombo, Paola
Nobile, Maria
author_sort Grazioli, Silvia
collection PubMed
description Recently, there has been an increase in telemedicine applied to child neuropsychiatry, such as the use of online platforms to collect remotely case histories and demographic and behavioral information. In the present proof-of-concept study, we aimed to understand to what extent information parents and teachers provide through online questionnaires overlaps with clinicians’ diagnostic conclusions on attention-deficit/hyperactivity disorder (ADHD). Moreover, we intended to explore a possible role that autism spectrum disorders (ASD) symptoms played in this process. We examined parent- and teacher-rated questionnaires collected remotely and an on-site evaluation of intelligence quotients from 342 subjects (18% females), aged 3–16 years, and referred for suspected ADHD. An easily interpretable machine learning model—decision tree (DT)—was built to simulate the clinical process of classifying ADHD/non-ADHD based on collected data. Then, we tested the DT model’s predictive accuracy through a cross-validation approach. The DT classifier’s performance was compared with those that other machine learning models achieved, such as random forest and support vector machines. Differences in ASD symptoms in the DT-identified classes were tested to address their role in performing a diagnostic error using the DT model. The DT identified the decision rules clinicians adopt to classify an ADHD diagnosis with an 82% accuracy rate. Regarding the cross-validation experiment, our DT model reached a predictive accuracy of 74% that was similar to those of other classification algorithms. The caregiver-reported ADHD core symptom severity proved the most discriminative information for clinicians during the diagnostic decision process. However, ASD symptoms were a confounding factor when ADHD severity had to be established. Telehealth procedures proved effective in obtaining an automated output regarding a diagnostic risk, reducing the time delay between symptom detection and diagnosis. However, this should not be considered an alternative to on-site procedures but rather as automated support for clinical practice, enabling clinicians to allocate further resources to the most complex cases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00787-023-02145-4.
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spelling pubmed-98751922023-01-25 Exploring telediagnostic procedures in child neuropsychiatry: addressing ADHD diagnosis and autism symptoms through supervised machine learning Grazioli, Silvia Crippa, Alessandro Rosi, Eleonora Candelieri, Antonio Ceccarelli, Silvia Busti Mauri, Maddalena Manzoni, Martina Mauri, Valentina Trabattoni, Sara Molteni, Massimo Colombo, Paola Nobile, Maria Eur Child Adolesc Psychiatry Original Contribution Recently, there has been an increase in telemedicine applied to child neuropsychiatry, such as the use of online platforms to collect remotely case histories and demographic and behavioral information. In the present proof-of-concept study, we aimed to understand to what extent information parents and teachers provide through online questionnaires overlaps with clinicians’ diagnostic conclusions on attention-deficit/hyperactivity disorder (ADHD). Moreover, we intended to explore a possible role that autism spectrum disorders (ASD) symptoms played in this process. We examined parent- and teacher-rated questionnaires collected remotely and an on-site evaluation of intelligence quotients from 342 subjects (18% females), aged 3–16 years, and referred for suspected ADHD. An easily interpretable machine learning model—decision tree (DT)—was built to simulate the clinical process of classifying ADHD/non-ADHD based on collected data. Then, we tested the DT model’s predictive accuracy through a cross-validation approach. The DT classifier’s performance was compared with those that other machine learning models achieved, such as random forest and support vector machines. Differences in ASD symptoms in the DT-identified classes were tested to address their role in performing a diagnostic error using the DT model. The DT identified the decision rules clinicians adopt to classify an ADHD diagnosis with an 82% accuracy rate. Regarding the cross-validation experiment, our DT model reached a predictive accuracy of 74% that was similar to those of other classification algorithms. The caregiver-reported ADHD core symptom severity proved the most discriminative information for clinicians during the diagnostic decision process. However, ASD symptoms were a confounding factor when ADHD severity had to be established. Telehealth procedures proved effective in obtaining an automated output regarding a diagnostic risk, reducing the time delay between symptom detection and diagnosis. However, this should not be considered an alternative to on-site procedures but rather as automated support for clinical practice, enabling clinicians to allocate further resources to the most complex cases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00787-023-02145-4. Springer Berlin Heidelberg 2023-01-25 /pmc/articles/PMC9875192/ /pubmed/36695897 http://dx.doi.org/10.1007/s00787-023-02145-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Contribution
Grazioli, Silvia
Crippa, Alessandro
Rosi, Eleonora
Candelieri, Antonio
Ceccarelli, Silvia Busti
Mauri, Maddalena
Manzoni, Martina
Mauri, Valentina
Trabattoni, Sara
Molteni, Massimo
Colombo, Paola
Nobile, Maria
Exploring telediagnostic procedures in child neuropsychiatry: addressing ADHD diagnosis and autism symptoms through supervised machine learning
title Exploring telediagnostic procedures in child neuropsychiatry: addressing ADHD diagnosis and autism symptoms through supervised machine learning
title_full Exploring telediagnostic procedures in child neuropsychiatry: addressing ADHD diagnosis and autism symptoms through supervised machine learning
title_fullStr Exploring telediagnostic procedures in child neuropsychiatry: addressing ADHD diagnosis and autism symptoms through supervised machine learning
title_full_unstemmed Exploring telediagnostic procedures in child neuropsychiatry: addressing ADHD diagnosis and autism symptoms through supervised machine learning
title_short Exploring telediagnostic procedures in child neuropsychiatry: addressing ADHD diagnosis and autism symptoms through supervised machine learning
title_sort exploring telediagnostic procedures in child neuropsychiatry: addressing adhd diagnosis and autism symptoms through supervised machine learning
topic Original Contribution
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875192/
https://www.ncbi.nlm.nih.gov/pubmed/36695897
http://dx.doi.org/10.1007/s00787-023-02145-4
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