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Analysis of institutional authors

Quesada-López, AAuthorSujar, ACorresponding Author

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April 25, 2024
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A systematic review on the application of machine learning models in psychometric questionnaires for the diagnosis of attention deficit hyperactivity disorder

Publicated to:European Journal Of Neuroscience. 60 (3): 4115-4127 - 2024-02-20 60(3), DOI: 10.1111/ejn.16288

Authors: Caselles-Pina, Lucia; Quesada-Lopez, Alejandro; Sujar, Aaron; Hernandez, Eva Maria Garzon; Delgado-Gomez, David

Affiliations

Univ Autonoma Madrid, Fac Psychol - Author
Univ Carlos III Madrid, Dept Stat - Author
Univ Rey Juan Carlos, Dept Informat & Estadist - Author

Abstract

Attention deficit hyperactivity disorder is one of the most prevalent neurodevelopmental disorders worldwide. Recent studies show that machine learning has great potential for the diagnosis of attention deficit hyperactivity disorder. The aim of the present article is to systematically review the scientific literature on machine learning studies for the diagnosis of attention deficit hyperactivity disorder, focusing on psychometric questionnaire tools. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were adopted. The review protocol was registered in the PROSPERO database. A search was conducted in three databases-Web of Science Core Collection, Scopus and Pubmed-with the aim of identifying studies that apply ML techniques to support the diagnosis of attention deficit hyperactivity disorder. A total of 17 empirical studies were found that met the established inclusion criteria. The results showed that machine learning can be used to increase the accuracy of attention deficit hyperactivity disorder diagnosis. Machine learning techniques are useful and effective strategies that can complement traditional diagnostics in patients with attention deficit hyperactivity disorder. This systematic review has highlighted the importance of attention deficit hyperactivity disorder diagnosis based on machine learning techniques as a complement to traditional assessment. Advances in machine learning will help make the diagnosis of attention deficit hyperactivity disorder much more simple and effective. Future machine learning studies in attention deficit hyperactivity disorder should improve the interpretability of their results to make them more accessible to health specialists. image

Keywords

AdhdAttention deficit disorder with hyperactivityAttention deficit hyperactivity disorderDeficit/hyperactivity disorderDiagnosisHumansMachine learningNeurodevelopmental disordersPsychometricsQuestionnaireRandom forestRegressionReliabilityReviewScaleSurveys and questionnairesTrees

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal European Journal Of Neuroscience due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position , thus managing to position itself as a Q2 (Segundo Cuartil), in the category Neuroscience (Miscellaneous). Notably, the journal is positioned en el Cuartil Q3 for the agency WoS (JCR) in the category Neurosciences.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-08-02:

  • WoS: 2
  • Scopus: 2
  • Europe PMC: 1

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-08-02:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 24.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 31 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

the author responsible for correspondence tasks has been Sújar Garrido, Aarón.