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

Abstract

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.

Keywords: Attention deficit/hyperactivity disorder; Autism spectrum disorders; Diagnostic process; Machine learning; Telehealth.

Fig. 1

Diagnostic process scheme. Graphic design of diagnostic process at the Regional Center for ADHD

Fig. 2

ADHD decision tree results. Representation of the machine learning algorithm results. ADHD Attention Deficit and Hyperactivity Disorder, CBCL Child Behavior Checklist/ 6–18, CPRS-R Conners' Parent Rating Scale-Revised, CTRS-R Conners' Teacher Rating Scale-Revised

Fig. 3

Decision tree results for TP and FN. Representation of the machine learning algorithm results. ADHD Attention Deficit and Hyperactivity Disorder, CBCL Child Behavior Checklist/ 6–18, CPRS-R Conners' Parent Rating Scale-Revised, CTRS-R Conners' Teacher Rating Scale-Revised

Fig. 4

Decision tree results for TN and FP. Representation of the machine learning algorithm results. ADHD Attention Deficit and Hyperactivity Disorder, CBCL Child Behavior Checklist/ 6–18, CPRS-R Conners' Parent Rating Scale-Revised, CTRS-R Conners' Teacher Rating Scale-Revised

Fig. 5

Total ASD symptoms. Differences in total ASD symptoms measured through SRS in the four classes. Note: median values are shown in the boxplots. *p < 0.05, **p < 0.01