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...
| Publicado en: | European Child & Adolescent Psychiatry Vol. 33; no. 1; pp. 139 - 150 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=174972287&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174972287 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10188827 EJ3 jtl: European Child & Adolescent Psychiatry issn: 10188827 maglogo: N pubinfo: dt: Jan2024 vid: 33 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 174972287 161475055 174972287 174972287 10.1007/s00787-023-02145-4 174972287 ppf: 139 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Exploring telediagnostic procedures in child neuropsychiatry: addressing ADHD diagnosis and autism symptoms through supervised machine learning. aug: au: 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 affil: https://ror.org/05ynr3m75 Child Psychopathology Unit, Scientific Institute, IRCCS Eugenio Medea, Via Don Luigi Monza, 20, Bosisio Parini, Lecco, Italy sug: subj: Child Psychiatry Neuropsychology Attention Deficit Hyperactivity Disorder Diagnosis Autism Spectrum Disorder Diagnosis Machine Learning Telemedicine Methods Decision Making, Clinical Italy Questionnaires Human Male Female Child, Preschool Child Adolescence Decision Trees Random Forest Algorithms Severity of Illness Data Analysis Software Descriptive Statistics Funding Source Retrospective Design Nonexperimental Studies Italy Parents Teachers Intelligence Tests Referral and Consultation Predictive Value of Tests Support Vector Machine Caregiver Attitudes Child, Preschool: 2-5 years Child: 6-12 years Adolescent: 13-18 years Male Female ab: 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. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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