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.

Use of Machine Learning Models to Differentiate Neurodevelopment Conditions Through Digitally Collected Data: Cross-Sectional Questionnaire Study

“…In contemporary diagnostic practice, the adoption of digital technologies has gained remarkable prominence, particularly in the digital administration of caregiver-rated questionnaires focused on capturing neurodevelopmental and behavioral symptoms [2,3]. This shift toward digitalization (often referred to as psycho-informatics) is fueled by the gradual incorporation of computational devices into clinical workflows.…”

“…Over the past two decades, there has been a significant rise in the application of advanced classification methods, such as supervised machine learning (ML), to enhance diagnostic research in the behavioral sciences. [1,3,[7][8][9][10][11][12][13][14][15][16]. Most of these studies have applied ML-based models to different types of data (e.g., home videos, child/adult diagnostic testing), reaching excellent classification accuracies [12][13][14][15][16][17].…”

“…In this context, recent work from our group has effectively identified children who received a clinical diagnosis of ADHD with an accuracy of up to 82% based on parent-and teacher-reported child behavior filled online, using a DT model simple supervised ML approach, the decision tree [3]. Ben-Sasson and Yom-Tov [15] also adopted DT to address a possible early identification of ASD through online queries posed by parents suspecting that their children could have that diagnosis; the classifier achieved an area under the curve of 0.82, indicating good predictive accuracy for identifying ASD risk based on parental narratives.…”

“…1) Random forest models. RF models were performed through the 'randomForest' toolbox [24], as previously done [3]. RF is an ensemble learning technique that generates many decision trees (DTs) and aggregates the results.…”

Telemedicine use in the managment of ADHD patients.

“…В данном случае применение телемедицинских инструментов позволяет наиболее быстро получить данные, в том числе предварительного диагноза. Однако авторы считают, что машинное обучение в подобной структуре оказания медицинской помощи должно выступать только как помощь врачу в принятии решений, а не полностью заменять его [18]. Для сравнения качества диагностики с помощью телеконсультаций и очного приема McDermott S.M.…”