Overview of Machine Learning Methods in ADHD Prediction

Sethu, Navya; Vyas, Renu

doi:10.1007/978-981-15-2063-1_3

Cited by 14 publications

(8 citation statements)

References 74 publications

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“…Our algorithm reached a very good accuracy (82%) in correctly identifying children which either did or did not receive a diagnosis of ADHD at the end of the clinical evaluation. The present accuracy is in line with previous ML works which highlighted the possibility of accurately discriminating subjects with and without ADHD [13,34,35]. However, earlier research was based on biological, neurophysiological, or behavioral data collected on-site.…”

Section: Discussionsupporting

confidence: 91%

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: Discussionsupporting

confidence: 91%

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

Grazioli

Crippa

Rosi

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The main problem was the long delay between commands. The results established that [7], this scenario can be used in more experiments with people suffering from ADHD investigating the therapeutic effectiveness of the prototype.…”

Section: E Attention and Memory Therapysupporting

confidence: 53%

“…In recent years, non-pharmacological treatments, such as cognitive training, neurofeedback, and behavioral interventions, have gained prominence as they have been accepted by therapists as useful alternatives to avoid the use of medications in children, which in many cases can cause dependence [2,3]. Several nonpharmacological treatments to treat these NND have implemented technological developments such as the Internet of Things (IoT) [4][5][6], Artificial Intelligence (AI) [7][8][9][10], Virtual Reality (VR) [11][12][13][14], Augmented Reality (AR) [15][16][17], and Robotics [18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Robotic Technologies in ADHD Care: Literature Review

et al. 2022

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Robotics has made it possible to change and improve many support processes for vulnerable people in different settings. In recent years, its use has been oriented toward supporting therapeutic interventions of neurodevelopmental disorders (NDD), including attention deficit hyperactivity disorder (ADHD). This review of the literature highlights how advances in robotics have evolved in different scenarios of ADHD treatment, its collaboration with other emerging technologies, its results, its limitations, and the research challenges for the future development of robotics in the field of supporting children with ADHD. The authors conducted a literature review based on the location of keywords 'robotics' and several NNDs such as 'ADHD', 'Autism Spectrum Disorder (ASD)', 'cerebral palsy', and 'dementia' in titles, abstracts, and introduction of scientific articles in the Scopus and Web of Science (WoS) database. The reviewed literature was classified according to the type of therapy supported by the robots, the type of robot and the associated technologies. From this analysis, we can solve the research question: Which types of robots have the potential for specific applications in ADHD treatment? Furthermore, this article shows that despite favorable technical results, robotic technologies that support ADHD therapies require significant improvements in terms of scalability, human-machine interaction, and treatment and processing of acquired information to be applied effectively in real-world therapies. The most significant research challenges are proposed to drive research efforts to develop new approaches to enable robotic assistants to participate in ADHD therapies.

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“…ADHD is diagnosed on the basis of various rating scales developed by experts. Additionally, MRI models are described to be used to examine the anatomical and functional features of the ADHD brain and the effects of drugs 34 . This study is a literature summary of machine learning for ADHD diagnosis and provides various information about diagnostic approaches.…”

Section: Discussionmentioning

confidence: 99%

Application of deep learning and classical machine learning methods in the diagnosis of attention deficit hyperactivity disorder according to temperament features

Altun

Alkan

Altun

2022

Concurrency and Computation

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Attention deficit hyperactivity disorder (ADHD) is a common childhood neurodevelopmental disorder with symptoms of attention deficit, hyperactivity and impulsivity, with a prevalence of 8%-12% worldwide. Various studies have revealed that there may be a relationship between ADHD and temperament traits in the etiology of ADHD, which has a multifactorial etiology. According to our knowledge, there is no study in the literature that determines the use of machine learning methods in diagnosing ADHD uses a data set created with temperament characteristics. Different methods were used in this first study. The study included 60 ADHD patients and 60 control group children. The test scores of these children were collected from the Department of Child and Adolescent Psychiatry, Kahramanmaraş Sütçü İmam University Medical Faculty, after obtaining the necessary ethics committee permission. ADHD diagnosis was made according to DSM-5 classification. According to temperament characteristics, the highest classification success in ADHD diagnosis was calculated as 92.5% in decision tree method. Long short term memory (LSTM), one of the deep learning methods, achieved 88% classification success. The success of both methods is quite high and they have been compared with some ADHD classification studies in the literature.

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Overview of Machine Learning Methods in ADHD Prediction

Cited by 14 publications

References 74 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

Robotic Technologies in ADHD Care: Literature Review

Application of deep learning and classical machine learning methods in the diagnosis of attention deficit hyperactivity disorder according to temperament features

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