Predicción del TDAH con Aprendizaje de Máquinas: Revisión Sistemática de Literatura

• Quintero-López, Catalina ^[1] ; Gil Vera, Víctor Daniel ^[1] ; Cerpa Bernal, Rafael Mauricio ^[2] ; Herrera Martínez, Marcelo ^[3]
  1. [1] Fundación Universitaria Luis Amigó

     Fundación Universitaria Luis Amigó

     Colombia

     
  2. [2] Universidad San Buenaventura, Facultad de Ingeniería, Bogotâ, Colombia.
  3. [3] Universidad San Buenaventura, Doctorado en Neurociencia Aplicada y Comportamiento, Grupo de Investigación Acústica Aplicada, Bogotá, Colombia.

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• Localización: Revista Argentina de Ciencias del Comportamiento ( RACC ), ISSN-e 1852-4206, Vol. 16, Nº. 3, 2024 (Ejemplar dedicado a: Revista Argentina de Ciencias del Comportamiento), págs. 14-32
• Idioma: español
• DOI: 10.32348/1852.4206.v16.n3.42221
• Títulos paralelos:
  • Prediction of ADHD with Machine Learning: A Systematic Literature Review Abstract
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• Resumen
  • español

    El Trastorno por Déficit de Atención e Hiperactividad (TDAH) tiene una prevalencia estimada del 5.3% en la población mundial; su diagnóstico se basa en evaluaciones clínicas, conductuales y resultados de pruebas psicométricas. Recientemente el Aprendizaje de Máquinas (AM) se ha empleado para detectar diversas condiciones neuropsiquiátricas, brindando una mayor precisión diagnóstica. El objetivo de este estudio fue realizar una revisión sistemática de la literatura (RSL) sobre la detección del TDAH en infantes con AM. Con base en la metodología PRISMA, se seleccionaron 30 publicaciones de WebofScience (WoS) y Scopus, que cumplieron los criterios de elegibilidad. Se concluye que la técnica más empleada para detectar el TDAH fue Máquinas de Vectores de Soporte, el modelo con el mejor desempeño se obtuvo de pruebas psicométricas. Un diagnóstico certero y temprano del TDAH previene complicaciones a largo plazo, problemas emocionales, académicos y sociales, así como el desarrollo de una estructura antisocial.

  • English

    Attention Deficit Hyperactivity Disorder (ADHD) affects approximately 13% of the child population; its diagnosis is based on clinical and behavioral assessments and psychometrics test results. Recently, Machine Learning (ML) has been used to detect various neuropsychiatric conditions, providing greater diagnostic accuracy. The aim of this research was to conduct a systematic literature review (SLR) on the detection of ADHD in infants’ witch ML. Based on the PRISMA methodology, 30 publications were selected from Web of Science (WoS) and Scopus which met the eligibility criteria. This paper concludes that the most used technique for ADHD detection was Support Vector Machines, the best-performing model was obtained from psychometrics test. An accurate and early diagnosis of ADHD prevents long-term complications; emotional, academic, and social problems, as well as the development of an antisocial structure.

• Referencias bibliográficas
  • Aggensteiner, P. M., Brandeis, D., Millenet, S., Hohmann, S., Ruckes, C., Beuth, S., Albrecht, B., Schmitt, G., Schermuly, S., Wörz, S., Gevensleben,...
  • *Amado-Caballero, P., Casaseca-De-La-Higuera, P., Alberola-Lopez, S., Andres-De-Llano, J. M., Villalobos, J. A. L., Garmendia-Leiza, J. R.,...
  • American Psychiatric Association (2021). Diagnostic and Statistical Manual of Mental Disorders DSM-5-TRTM (5th Ed., Text Revision). Editorial...
  • American Psychiatric Association (1994). Diagnostic and Statistical Manual of Mental Disorders DSM-4 (4th Ed.). American Psychiatric Publishing,...
  • APA PsycInfo (2021). Thesaurus of Psychological Index Terms. https://www.apa.org/pubs/databases/training/thesaurus
  • Arpaia, P., Covino, A., Cristaldi, L., Frosolone, M., Gargiulo, L., Mancino, F., Mantile, F.,& Moccaldi, N. (2022). A systematic review...
  • Artrith, N., Butler, K. T., Coudert, F. X., Han, S., Isayev, O., Jain, A., & Walsh, A. (2021). Best practices in machine learning for...
  • Baghaei, P., Ravand, H., & Nadri, M. (2019). Is the d2 test of attention Rasch scalable? Analysis with the Rasch Poisson counts model....
  • Baker, B. H., Joo, Y. Y., Park, J., Cha, J., Baccarelli, A. A., & Posner, J. (2023). Maternal age at birth and child attention‐deficit...
  • *Bledsoe, J. C., Xiao, C., Chaovalitwongse, A., Mehta, S., Grabowski, T. J., Semrud-Clikeman, M., Pliszka, S., & Breiger, D. (2020). Diagnostic...
  • *Boroujeni, Y. K., Rastegari, A. A., & Khodadadi, H. (2019). Diagnosis of attention deficit hyperactivity disorder using non-linear analysis...
  • Bosch, F., & Guardiola, E. (2003). Lista de comprobación (checklist) abreviada para la evaluación de artículos de investigación biomédica...
  • Brikell, I., Kuja-Halkola, R., & Larsson, H. (2019). Heritability of attention-deficit hyperactivity disorder across the lifespan. European...
  • Burgos, N., & Colliot, O. (2020). Machine learning for classification and prediction of brain diseases: recent advances and upcoming challenges....
  • Cao, M., Martin, E., & Li, X. (2023). Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding...
  • *Cervantes-Henríquez, M. L., Acosta-López, J. E., Martinez, A. F., Arcos-Burgos, M., Puentes-Rozo, P. J., &Vélez, J. I. (2022). Machine...
  • *Chang, Y., Stevenson, C., Chen, I. C., Lin, D. S., & Ko, L. W. (2022). Neurological state changes indicative of ADHD in children learned...
  • Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., Cohen, Z., Belgrave, D., DeRubeis, R., Iniesta, R., Dwyer,...
  • Conejero, I., Jaussent, I., Lopez, R., Guillaume, S., Olié, E., Hebbache, C., Cohen, R.F., Kahn, J.P., Leboyer, M., Courtet, P., & Lopez-Castroman,...
  • Cortese, S. (2022). Treatment of ADHD in preschool children. The Lancet Child & Adolescent Health, 6(12), 830-831. https://doi.org/10.1016/S2352-4642...
  • Dawson, A. E., Wymbs, B. T., Evans, S. W., & DuPaul, G. J. (2019). Exploring how adolescents with ADHD use and interact with technology....
  • *Deshpande, G., Wang, P., Rangaprakash, D., & Wilamowski, B. (2015). Fully connected cascade artificial neural network architecture for...
  • *Du, J., Wang, L., Jie, B., & Zhang, D. (2016). Network-based classification of ADHD patients using discriminative subnetwork selection...
  • *Duda, M., Haber, N., Daniels, J., & Wall, D. P. (2017). Crowdsourced validation of a machine-learning classification system for autism...
  • *Faedda, G. L., Ohashi, K., Hernandez, M., McGreenery, C. E., Grant, M. C., Baroni, A., Polcari, A., & Teicher, M. H. (2016). Actigraph...
  • Fayyad, J., Sampson, N. A., Hwang, I., Adamowski, T., Aguilar-Gaxiola, S., Al-Hamzawi, A., Andrade, L. H.G., Borges, G., de Girolamo, G.,...
  • Furzer, J., Dhuey, E., & Laporte, A. (2022). ADHD misdiagnosis: Causes and mitigators. Health Economics, 31(9), 1926-1953. https://doi.org/10.1002/hec.4555
  • *Ghiassian, S., Greiner, R., Jin, P., & Brown, M. R. G. (2016). Using functional or structural magnetic resonance images and personal...
  • Ging-Jehli, N. R., Ratcliff, R., & Arnold, L. E. (2021). Improving neurocognitive testing using computational psychiatry—A systematic...
  • *Goh, P. K., Elkins, A. R., Bansal, P. S., Eng, A. G., & Martel, M. M. (2023). Data-Driven Methods for Predicting ADHD Diagnosis and Related...
  • González, M. N., & Depaula, P. D. (2023). Parenting stress and coping strategies in mothers of children with Attention Deficit Hyperactivity...
  • Greven, C.U., Bralten, J., Mennes, M., O’Dwyer, L., van Hulzen, K.J.E., Rommelse, N., Schweren, L.J.S., Hoekstra, P.J., Hartman, C.A., Heslenfeld,...
  • Harkins, C. M., Handen, B. L., & Mazurek, M. O. (2022). The impact of the comorbidity of ASD and ADHD on social impairment. Journal of...
  • Harrison, A. G., & Edwards, M. J. (2023). The Ability of Self-Report Methods to Accurately Diagnose Attention Deficit Hyperactivity Disorder:...
  • Hermosillo, R.J.M., Mooney, M.A., Fezcko, E., Earl, E., Marr, M., Sturgeon, D., Perrone, A., Dominguez, O.M., Faraone, S.V., Wilmot, B., Nigg,...
  • Hoelzle, J. B., Ritchie, K. A., Marshall, P. S., Vogt, E. M., & Marra, D. E. (2019). Erroneous conclusions: The impact of failing to identify...
  • Holland, J., & Sayal, K. (2019). Relative age and ADHD symptoms, diagnosis and medication: a systematic review. European Child & Adolescent...
  • IEEE Advancing Technology for Humanity. (2020).IEEE Thesaurus.https://www.ieee.org/publications/services/thesaurus.html
  • Izzo, V. A., Donati, M. A., Novello, F., Maschietto, D., & Primi, C. (2019). The Conners 3–short forms: Evaluating the adequacy of brief...
  • *Jung, M., Mizuno, Y., Fujisawa, T. X., Takiguchi, S., Kong, J., Kosaka, H., & Tomoda, A. (2019). The Effects of COMT Polymorphism on...
  • *Jung, M., Tu, Y., Park, J., Jorgenson, K., Lang, C., Song, W., & Kong, J. (2019). Surface-based shared and distinct resting functional...
  • Kelleher, J. D., Mac Namee, B., & D'arcy, A. (2020). Fundamentals of machine learning for predictive data analytics: algorithms, worked...
  • Kim, W. P., Kim, H. J., Pack, S. P., Lim, J. H., Cho, C. H., & Lee, H. J. (2023). Machine Learning-Based Prediction of Attention-Deficit/Hyperactivity...
  • *Kim, S., Lee, H.-K., & Lee, K. (2021). Screening of mood symptoms using MMPI-2-RF scales: An application of machine learning techniques....
  • *Koh, J. E. W., Ooi, C. P., Lim-Ashworth, N. S., Vicnesh, J., Tor, H. T., Lih, O. S., Tan, R. S., Acharya, U. R., & Fung, D. S. S. (2022)....
  • Koppe, G., Meyer-Lindenberg, A., & Durstewitz, D. (2021). Deep learning for small and big data in psychiatry. Neuropsychopharmacology,...
  • *Kuang, D., & He, L. (2014, November). Classification on ADHD with deep learning. En 2014 International Conference on Cloud Computing...
  • *Lin, I. C., Chang, S. C., Huang, Y. J., Kuo, T. B. J., & Chiu, H. W. (2023). Distinguishing different types of attention deficit hyperactivity...
  • Loh, H. W., Ooi, C. P., Barua, P. D., Palmer, E. E., Molinari, F., & Acharya, U. R. (2022). Automated detection of ADHD: Current trends...
  • *Lohani, D. C., & Rana, B. (2023). ADHD Diagnosis using structural Brain MRI and Personal Characteristic Data with Machine Learning Framework....
  • Lugoboni, F., & Tinghino, B. (2022). Combined prevention for substance use and mental health problems in youth: A glance at two conditions...
  • Luo, N., Luo, X., Zheng, S., Yao, D., Zhao, M., Cui, Y., Zhu, Y., Calhoun, V.D., Sun, L., & Sui, J. (2023). Aberrant brain dynamics and...
  • Mariggió, M. A., Palumbi, R., Vinella, A., Laterza, R., Petruzzelli, M. G., Peschechera, A., Gabellone, A., Gentile, O., Vincenti, A., &...
  • Martel, M. M., Schimmack, U., Nikolas, M., &Nigg, J. T. (2015). Integration of symptom ratings from multiple informants in ADHD diagnosis:...
  • Martella, D., Aldunate, N., Fuentes, L.J., & Sánchez-Pérez, N. (2020).Arousal and Executive Alterations in Attention Deficit Hyperactivity...
  • Mengi, M., & Malhotra, D. (2022). Artificial Intelligence Based Techniques for the Detection of Socio-Behavioral Disorders: A Systematic...
  • *Mikolas, P., Vahid, A., Bernardoni, F., Süß, M., Martini, J., Beste, C., & Bluschke, A. (2022). Training a machine learning classifier...
  • *Mohammadi, M. R., Khaleghi, A., Nasrabadi, A. M., Rafieivand, S., Begol, M., & Zarafshan, H. (2016). EEG classification of ADHD and normal...
  • Mu, S., Wu, H., Zhang, J., & Chang, C. (2022). Structural brain changes and associated symptoms of ADHD subtypes in children. Cerebral...
  • *O’Mahony, N., Florentino-Liano, B., Carballo, J. J., Baca-García, E., & Rodríguez, A. A. (2014). Objective diagnosis of ADHD using IMUs....
  • *Öztekin, I., Finlayson, M. A., Graziano, P. A., & Dick, A. S. (2021). Is there any incremental benefit to conducting neuroimaging and...
  • Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I. T., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan,...
  • Parke, E. M., Becker, M. L., Graves, S. J., Baily, A. R., Paul, M. G., Freeman, A. J., & Allen, D. N. (2021). Social cognition in children...
  • Pereira-Sanchez, V., & Castellanos, F. X. (2021). Neuroimaging in attention-deficit/hyperactivity disorder. Current Opinion in Psychiatry,...
  • Periyasamy, R., Vibashan, V. S., Varghese, G. T., & Aleem, M. A. (2021). Machine learning techniques for the diagnosis of attention-deficit/hyperactivity...
  • Polanczyk, G. V., Willcutt, E. G., Salum, G. A., Kieling, C., & Rohde, L. A. (2014). ADHD prevalence estimates across three decades: an...
  • Jothi Prabha, A., & Bhargavi, R. (2019). Prediction of dyslexia using machine learning—a research travelogue. En V. Nath & J. Mandal...
  • Quintero-López, C., Gil-Vera, V. D., Landinez-Martínez, D. A., Vargas-Gaviria, J. P., & Gómez-Muñoz, N. (2023). Predictive Neurocognitive...
  • *Qureshi, M. N. I., Oh, J., Min, B., Jo, H. J., & Lee, B. (2017). Multi-modal, multi-measure, and multi-class discrimination of ADHD with...
  • Scandurra, V., Emberti Gialloreti, L., Barbanera, F., Scordo, M. R., Pierini, A., & Canitano, R. (2019). Neurodevelopmental disorders...
  • Silva, M., & Graña, M. (2022). On Machine Learning for Autism Prediction from Functional Connectivity. En M. Choraś, R. S. Choraś, M....
  • *Slobodin, O., Yahav, I., & Berger, I. (2020). A Machine-Based Prediction Model of ADHD Using CPT Data. Frontiers in Human Neuroscience,...
  • Song, J., Leventhal, B. L., Koh, Y. J., Cheon, K. A., Hong, H. J., Kim, Y. K., Cho, K., Lim, E.C., Park, J., & Kim, Y. S. (2017). Cross-cultural...
  • Song, C., Jiang, Z. Q., Liu, D., & Wu, L. L. (2022). Application and research progress of machine learning in the diagnosis and treatment...
  • Sordo, S. Á., Garrido Hernansaiz, H., Cantero García, M., Sánchez Iglesias, I., González Moreno, J., & Santacreu, J. (2021). Validez de...
  • Sundaresan, A., Penchina, B., Cheong, S., Grace, V., Valero-Cabré, A., & Martel, A. (2021). Evaluating deep learning EEG-based mental...
  • Rae, T., Sanders, S., Doust, J., Beller, E., & Glasziou, P. (2015). Prevalence of attention-deficit/hyperactivity disorder: a systematic...
  • *Tor, H. T., Ooi, C. P., Lim-Ashworth, N. S., Wei, J. K. E. Jahmunah, V., Oh, S. L., Acharya, U. R., & Fung, D. S. S. (2021). Automated...
  • *Vaidya, C. J., You, X., Mostofsky, S., Pereira, F., Berl, M. M., & Kenworthy, L. (2020). Data-driven identification of subtypes of executive...
  • Vimalajeewa, D., McDonald, E., Bruce, S. A., & Vidakovic, B. (2022). Wavelet-based approach for diagnosing attention deficit hyperactivity...
  • Wang, M., & Chen, H. (2020). Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Applied Soft Computing,...
  • Weiss, F., Tidona, S., Carli, M., Perugi, G., & Scarselli, M. (2023). Triple Diagnosis of Attention-Deficit/Hyperactivity Disorder with...
  • Whitney, D. G., & Peterson, M. D. (2019). US national and state-level prevalence of mental health disorders and disparities of mental...
  • Yang, S., Ma, W., Pi, X., & Qian, S. (2019). A deep learning approach to real-time parking occupancy prediction in transportation networks...
  • Yasumura, A., Omori, M., Fukuda, A., Takahashi, J., Yasumura, Y., Nakagawa, E., Koike, T., Yamashita, Y., Miyajima, T., Koeda, T., Aihara,...
  • *Yeh, S. C., Lin, S. Y., Wu, E. H. K., Zhang, K. F., Xiu, X., Rizzo, A., & Chung, C. R. (2020). A Virtual-Reality System Integrated with...
  • *Yoo, J. H., Kim, J. I., Kim, B. N., & Jeong, B. (2020). Exploring characteristic features of attention-deficit/hyperactivity disorder:...
  • Young, S., Adamo, N., Ásgeirsdóttir, B. B., Branney, P., Beckett, M., Colley, W., Cubbim, S., Deeley, Q., Farrag, E., Gudjonsson, G., Hill,...
  • *Zhang-James, Y., Chen, Q., Kuja-Halkola, R., Lichtenstein, P., Larsson, H., & Faraone, S. V. (2020). Machine-Learning prediction of comorbid...
  • Zhang-James, Y., Razavi, A. S., Hoogman, M., Franke, B., &Faraone, S. V. (2023). Machine learning and MRI-based diagnostic models for...