A Web Platform for Standardized Data Acquisition, Processing, and Export in the Child Psychopathology Clinical Routine (MedicalBIT): Design and Implementation Study

Colombo, Paola; Ceccarelli, Silvia; Pacchiarini, Stefano; Cribellati, Stefano; Molteni, Massimo

doi:10.2196/36757

Cited by 3 publications

(9 citation statements)

References 26 publications

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“…Over the last few years, the digital innovation process and the COVID-19 pandemic spurred an increasing request for telehealth procedures [32,33]. The rst steps of the diagnostic process for ADHD may t this trend, since a thorough information collection regarding children's behaviors could be potentially performed remotely [11].…”

Section: Discussionmentioning

confidence: 99%

“…Online information collection and screening procedures should not be merely considered as an alternative to on-site diagnostic practice. Instead, telehealth can help effectively collect reliable caregivers' reports and obtain a subsequent automated output regarding a diagnostic risk factor [11]. Special attention should be given not only to the development of accurate diagnostic classi cation models but also to the factors that might lead to diagnostic misclassi cation.…”

Section: Discussionmentioning

confidence: 99%

“…Future research focused on the development of online platforms for remotely performed data collection is needed [11]. Developments of ML predictive models could also offer clinicians prompt feedback about the diagnostic risks associated with questionnaire scores.…”

Section: Future Directionsmentioning

confidence: 99%

“…To do this, we tested a decision tree (DT) classi cation, an interpretable machine learning (ML) algorithm, to analyze diagnostic data collected at the Scienti c Institute "IRCCS Eugenio Medea" Regional Center for ADHD [10]. Here we recently developed the rst Italian web-based screening tool to remotely administer digital clinical questionnaires in order to provide timely and effective support for the diagnostic process in the child neuropsychiatry eld [11]. Recent evidence has shown the advantages of ML algorithms as well-suited analytic techniques for digitally obtained diagnostic data within the progressive digitalization process under way in clinical practice over the past years [12].…”

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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

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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“…Furthermore, the onset of the COVID-19 pandemic has further supported adopting digital tools for telehealth support, particularly within the mental health sector [5]. Digitization also includes aspects that cut across diagnoses, such as socio-anamnestic information relevant to neurodevelopmental conditions [6].…”

Section: Use Of Machine Learning Models To Differentiate Neurodevelop...mentioning

confidence: 99%

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

Grazioli,

Crippa,

Buo

et al. 2024

JMIR Form Res

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Background: Child and adolescent psychopathology diagnosis involves a multifaceted approach, which integrates clinical observations, behavioral assessments, medical history, cognitive testing, and familial context information. Digital technologies, including online platforms for administering caregiver-rated questionnaires, are increasingly utilized in this field, especially during the first screening phase. With the rise of digital platforms for data collection, advanced psychopathology classification methods such as supervised machine learning (ML) have gained prominence in both research and clinical settings. This shift, recently referred to as psycho-informatics, has been facilitated by the gradual incorporation of computational devices into clinical workflows. However, an integrated telemedicine and ML approach is yet to be developed.Objective: This study aims to develop ML models for the classification of Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) diagnoses using online parent-reported socio-anamnestic data, with a primary focus on accuracy over interpretability. The objective is therefore to address the need for an innovative computational psychometrics framework that harnesses the potential of digital platforms and ML to improve the assessment of neurodevelopmental conditions. Methods:In this retrospective, single-center observational study, socio-anamnestic data were collected from 1688 children and adolescents referred for suspected neurodevelopmental conditions. The data included information on sociodemographic, clinical, environmental, and developmental factors, collected remotely through the first Italian online screening tool for neurodevelopmental disorders, the Medea Information and Clinical Assessment On-Line (MedicalBIT). Random Forest (RF) and Logistic Regression (LR) models were developed and evaluated using classification accuracy, sensitivity, specificity, and importance of independent variables.Results: RF models demonstrated robust accuracy, achieving 84% (95% CI: 82%-85%, P < .001) for ADHD and 86% (95% CI: 84%-87%, P < .001) for ASD classifications, with high sensitivities of 93% for ADHD and of 95% for ASD. In contrast, the LR models exhibited both lower accuracy (61%, 95% CI: 57%-64%, P < .001 for ADHD, and 63%, 5% CI: 60%-67%, P < .001 for ASD) and sensitivities (62% for ADHD and 68% for ASD). Intriguingly, the independent variables considered for classification differed in importance between the two models, reflecting the distinct approaches and capabilities of RF and LR. Conclusions:The present study provides evidence for the potential of an ML approach, especially when built on RF models, in enhancing the diagnostic process of child and adolescent psychopathology. While interpretability remains crucial, the developed approach might therefore provide valuable screening tools for clinicians, highlighting the significance of embedding computational techniques in the diagnostic process. This paves the way for the integration of AI-driven tools in...

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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. 2023

Eur Child Adolesc Psychiatry

View full text Add to dashboard Cite

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.

show abstract

A Web Platform for Standardized Data Acquisition, Processing, and Export in the Child Psychopathology Clinical Routine (MedicalBIT): Design and Implementation Study

Cited by 3 publications

References 26 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

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

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

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