Who predict ADHD with better diagnostic accuracy?: Parents or teachers?

Tahıllıoğlu, Akın; Bilaç, Öznur; Uysal, Taciser; Ercan, Eyüp Sabri

doi:10.1080/08039488.2020.1867634

Cited by 9 publications

(11 citation statements)

References 36 publications

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“…Within this workflow, ADHD characteristics are investigated—to a certain degree—through parent and teacher reports that could be digitally administered. A recent study demonstrated that parents and teachers showed similar diagnostic accuracy in predicting a clinical diagnosis when considering the ADHD Rating Scale-IV threshold to discriminate ADHD/non-ADHD condition [ 8 ]. However, parents with lower educational attainment showed worse diagnostic accuracy when compared both to parents with higher education levels and to teachers [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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

Grazioli

Crippa

Rosi

et al. 2023

Eur Child Adolesc Psychiatry

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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.

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Section: Introductionmentioning

confidence: 99%

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

Grazioli

Crippa

Rosi

et al. 2023

Eur Child Adolesc Psychiatry

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show abstract

“…Moreover, although the algorithm was totally naïve about the questionnaire cutoffs, in the upper nodes the DT identi ed scores that are in line with moderate and severe risk for ADHD, respectively 64 and 70 [19,23]. These ndings thus extend -for the rst time, in a telehealth setting-recent ndings which showed that caregivers' reports could reliably predict ADHD diagnosis [8].…”

Section: Discussionmentioning

confidence: 54%

“…Within this work ow, ADHD characteristics are investigated -to a certain degree-through parent and teacher reports that could be digitally administered. A recent study has demonstrated that parents and teachers show similar diagnostic accuracy in predicting a clinical diagnosis when the ADHD Rating Scale-IV threshold to discriminate ADHD/non-ADHD condition is considered [8]. However, parents with lower educational attainment showed worse diagnostic accuracy when compared to both parents with higher education levels and teachers [8].…”

Section: Introductionmentioning

confidence: 99%

“…A recent study has demonstrated that parents and teachers show similar diagnostic accuracy in predicting a clinical diagnosis when the ADHD Rating Scale-IV threshold to discriminate ADHD/non-ADHD condition is considered [8]. However, parents with lower educational attainment showed worse diagnostic accuracy when compared to both parents with higher education levels and teachers [8]. This effect could potentially be enhanced by remote collection of behavioral data because individuals with lower educational levels may face di culties accessing digital tools [9].…”

Section: Introductionmentioning

confidence: 99%

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

Grazioli

Crippa

Rosi

et al. 2022

Preprint

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Recently there has been an increase in telemedicine applied to child neuropsychiatry, such as the use of online platforms to remotely collect anamnestic and behavioral information. In the present proof-of-concept study, we aimed to understand to what extent information provided by parents and teachers through online questionnaires overlaps with clinicians’ diagnostic conclusions on Attention-Deficit/Hyperactivity Disorder (ADHD). Moreover, we intended to explore a possible role played by autism spectrum disorders (ASD) symptoms 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 to 16 years, referred for suspected ADHD. A machine learning model (decision tree, DT) was built to simulate the clinical process of classifying ADHD/non-ADHD. 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 adopted by clinicians to classify ADHD diagnosis and proved accurate in 82% of our subjects. 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 as an alternative to on-site procedures but as automated support for clinical practice, enabling clinicians to allocate more resources to the most complex cases.

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“…The study by Lúcio PS et al [ 14 ] with pre-school children also validated this result and indicated that there was approximate measurement invariance for teachers' assessments over a longitudinal interval of 6 months. More specifically, recent study suggested that girls were more predictable than boys in terms of ADHD according to both parents and teachers [ 15 ]. Other scales may also suffer from the above problems.…”

Section: Introductionmentioning

confidence: 99%

A Wearable Diagnostic Assessment System vs. SNAP-IV for the auxiliary diagnosis of ADHD: a diagnostic test

et al. 2022

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Objective We design a diagnostic test to evaluate the effectiveness and accuracy of A non-intrusive Wearable Diagnostic Assessment System versus SNAP-IV for auxiliary diagnosis of children with ADHD. Methods This study included 55 children aged 6–16 years who were clinically diagnosed with ADHD by DSM-5, and 55 healthy children (typically developing). Each subject completes 10 tasks on the WeDA system (Wearable Diagnostic Assessment System) and Parents of each subject complete the SNAP-IV scale. We will calculate the validity indexes, including sensitivity, specificity, Youden's index, likelihood ratio, and other indexes including predictive value, diagnostic odds ratio, diagnostic accuracy and area under the curve [AUC] to assess the effectiveness of the WeDA system as well as the SNAP-IV. Results The sensitivity (94.55% vs. 76.36%) and the specificity (98.18% vs. 80.36%) of the WeDA system were significantly higher than the SNAP-IV. The AUC of the WeDA system (0.964) was higher than the SNAP-IV (0.907). There is non-statistically significant difference between groups (p = 0.068), and both of them have high diagnostic accuracy. In addition, the diagnostic efficacy of the WeDA system was higher than that of SNAP-IV in terms of the Youden index, diagnostic accuracy, likelihood ratio, diagnostic odds ratio and predictive value. Conclusion The advantages of the WeDA system in terms of diagnostic objectivity, scientific design and ease of operation make it a promising system for widespread use in clinical practice.

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Who predict ADHD with better diagnostic accuracy?: Parents or teachers?

Cited by 9 publications

References 36 publications

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

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

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

A Wearable Diagnostic Assessment System vs. SNAP-IV for the auxiliary diagnosis of ADHD: a diagnostic test

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