Designing a Decision Support System for Distinguishing ADHD from Similar Children Behavioral Disorders

Delavarian, Mona; Towhidkhah, Farzad; Dibajnia, Parvin; Gharibzadeh, Shahriar

doi:10.1007/s10916-010-9594-9

Cited by 15 publications

(1 citation statement)

References 20 publications

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“…Clinicians therefore face many challenges in providing accurate assessment and diagnosis of ADHD in practice. For example, there is considerable overlap between ADHD symptoms and those of other conditions; inattention and restlessness may be observed in depression (Delavarian et al , 2012), anxiety disorders (Connolly and Bernstein, 2007), specific language disorders (Pham and Riviere, 2015) and intellectual disabilities (Simonoff et al , 2007; McClain et al , 2017). Results from national surveys also indicate that over half of children with ADHD have at least one additional neurodevelopmental or psychiatric condition, with the most common being specific learning difficulties, anxiety disorders and oppositional defiant disorder (Reale et al , 2017; Zablotsky et al , 2018).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a diagnostic ADHD pathway in a community child mental health service in South London

Barnes¹,

Wretham

Sedgwick

et al. 2020

MHRJ

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Purpose Clinicians working in UK child mental health services are faced with several challenges in providing accurate assessment and diagnosis of attention deficit hyperactivity disorder (ADHD). Within the South London & Maudsley (SLaM) NHS Trust, community Child & Adolescent Mental Health Services (CAMHS) are developing structured pathways for assessing and diagnosing ADHD in young people. To date, these pathways have not been formally evaluated. The main aims of this evaluation are to evaluate all ADHD referrals made to the service in an 18-month period, including the number of completed assessments and proportion of children diagnosed with ADHD; and investigate adherence to the National Institute for Clinical Excellence (NICE) guideline for diagnosing ADHD in children and young people. Design/methodology/approach Retrospective data analysis was performed using service databases and electronic patient records. Adherence to the clinical guideline was measured using the NICE data collection tool for diagnosing ADHD in children and young people. All completed ADHD assessments were compared to four key recommendation points in the guideline. Findings Within the time frame, 146 children aged 4-17 years were referred and accepted for an ADHD assessment. Of these, 92 families opted in and were seen for an initial appointment. In total, 36 ADHD assessments were completed, of which 19 children received a diagnosis of ADHD and 17 did not. Aside from structured recording of ADHD symptoms based on ICD-10 criteria (69%) and reporting of functional impairment (75%), adherence to all guidance points was above 90%. The study also found that although a greater proportion of children referred to the service were male and identified as White, these differences narrowed upon receipt of ADHD diagnosis. Research limitations/implications Relationship to the existing literature is discussed in relation to the assessment process, demographic characteristics and rates of co-occurrence. Practical implications The findings demonstrate that in child mental health services, gold standard practice for diagnosing ADHD should be the adoption of clear, protocol-driven pathways to support appropriate access and treatment for young people and their families. Originality/value This article is unique in that it is, to the best of the authors’ knowledge, the first to describe and report clinician-adherence to a structured pathway for diagnosing ADHD in young people within a community CAMHS service in South London.

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Section: Introductionmentioning

confidence: 99%

Evaluation of a diagnostic ADHD pathway in a community child mental health service in South London

Barnes¹,

Wretham

Sedgwick

et al. 2020

MHRJ

View full text Add to dashboard Cite

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Performance Analysis of Machine Learning and Soft Computing Techniques in Diagnosis of Behavioral Disorders

Monga

Sharma

2022

Lecture Notes in Electrical Engineering

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Dual System for Enhancing Cognitive Abilities of Children with ADHD Using Leap Motion and eye-Tracking Technologies

2017

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Attention Deficit Hyperactivity Disorder (ADHD) is a brain disorder marked by an ongoing pattern of inattention and/or hyperactivity-impulsivity that affects with development or functioning. It affects 3-5% of all American and European children. The objective of this paper is to develop and test a dual system for the rehabilitation of cognitive functions in children with ADHD. A technological platform has been developed using the ". NET framework", which makes use of two physiological sensors, -an eye-tracker and a hand gesture recognition sensor- in order to provide children with the opportunity to develop their learning and attention skills. The two physiological sensors we utilized for the development are the Tobii X1 Light Eye Tracker and the Leap Motion. SUS and QUIS questionnaires have been carried out. 19 users tested the system and the average age was 10.88 years (SD = 3.14). The results obtained after tests were performed were quite positive and hopeful. The learning of the users caused by the system and the interfaces item got a high punctuation with a mean of 7.34 (SD = 1.06) for SUS questionnaire and 7.73 (SD = 0.6) for QUIS questionnaire. We didn't find differences between boys and girls. The developed multimodal rehabilitation system can help to children with attention deficit and learning issues. Moreover, the teachers may utilize this system to track the progression of their students and see their behavior.

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Designing a Decision Support System for Distinguishing ADHD from Similar Children Behavioral Disorders

Cited by 15 publications

References 20 publications

Evaluation of a diagnostic ADHD pathway in a community child mental health service in South London

Evaluation of a diagnostic ADHD pathway in a community child mental health service in South London

Performance Analysis of Machine Learning and Soft Computing Techniques in Diagnosis of Behavioral Disorders

Dual System for Enhancing Cognitive Abilities of Children with ADHD Using Leap Motion and eye-Tracking Technologies

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