Predicción del TDAH con Aprendizaje de Máquinas: Revisión Sistemática de Literatura
Contenido principal del artículo
Resumen
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.
Detalles del artículo
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
La RACC aplicará la licencia internacional de atribuciones comunes creativas (Reconocimiento 4.0 Internacional: https://creativecommons.org/licenses/by/4.0/).
Bajo esta licencia, se permite cualquier explotación de la obra, incluyendo la explotación con fines comerciales y la creación de obras derivadas, la distribución de las cuales también está permitida sin ninguna restricción. Esta licencia es una licencia libre según la Freedom Defined. La única condición es que siempre y en todos los casos se cite a los autores y a la fuente original de publicación (i.e., RACC). Esta licencia fue desarrollada para facilitar el acceso abierto, gratuito y libre a trabajos originales científicos y artísticos.
Cómo citar
Referencias
Aggensteiner, P. M., Brandeis, D., Millenet, S., Hohmann, S., Ruckes, C., Beuth, S., Albrecht, B., Schmitt, G., Schermuly, S., Wörz, S., Gevensleben, H., Freitag, C. M., Banaschewski, T., Rothenberger, A., Strehl, U., & Holtmann, M. (2019). Slow cortical potentials neurofeedback in children with ADHD: comorbidity, self-regulation and clinical outcomes 6 months after treatment in a multicenter randomized controlled trial. European Child & AdolescentPsychiatry, 28, 1087-1095. https://doi.org/10.1007/s00787-018-01271-8
*Amado-Caballero, P., Casaseca-De-La-Higuera, P., Alberola-Lopez, S., Andres-De-Llano, J. M., Villalobos, J. A. L., Garmendia-Leiza, J. R., & Alberola-Lopez, C. (2020). Objective ADHD Diagnosis Using Convolutional Neural Networks over Daily-Life Activity Records. IEEE Journal of Biomedical and Health Informatics, 24(9), 2690–2700. https://doi.org/10.1109/JBHI.2020.2964072
American Psychiatric Association (2021). Diagnostic and Statistical Manual of Mental Disorders DSM-5-TRTM (5th Ed., Text Revision). Editorial Médica Panamericana.
American Psychiatric Association (1994). Diagnostic and Statistical Manual of Mental Disorders DSM-4 (4th Ed.). American Psychiatric Publishing, Inc.
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 on feature extraction in electroencephalography-based diagnostics and therapy in attention deficit hyperactivity disorder. Sensors, 22(13), Artículo 4934. https://doi.org/10.3390/s22134934
Artrith, N., Butler, K. T., Coudert, F. X., Han, S., Isayev, O., Jain, A., & Walsh, A. (2021). Best practices in machine learning for chemistry. Nature Chemistry, 13, 505-508. https://doi.org/10.1038/s41557-021-00716-z
Baghaei, P., Ravand, H., & Nadri, M. (2019). Is the d2 test of attention Rasch scalable? Analysis with the Rasch Poisson counts model. Perceptual and Motor Skills, 126(1), 70-86. https://doi.org/10.1177/0031512518812183
Baker, B. H., Joo, Y. Y., Park, J., Cha, J., Baccarelli, A. A., & Posner, J. (2023). Maternal age at birth and child attention‐deficit hyperactivity disorder: causal association or familial confounding? Journal of Child Psychology and Psychiatry, 64(2), 299-310. https://doi.org/10.1111/jcpp.13726
*Bledsoe, J. C., Xiao, C., Chaovalitwongse, A., Mehta, S., Grabowski, T. J., Semrud-Clikeman, M., Pliszka, S., & Breiger, D. (2020). Diagnostic Classification of ADHD Versus Control: Support Vector Machine Classification Using Brief Neuropsychological Assessment. Journal of Attention Disorders, 24(11), 1547–1556. https://doi.org/10.1177/1087054716649666
*Boroujeni, Y. K., Rastegari, A. A., & Khodadadi, H. (2019). Diagnosis of attention deficit hyperactivity disorder using non-linear analysis of the EEG signal. IET Systems Biology, 13(5), 260–266. https://doi.org/10.1049/iet-syb.2018.5130
Bosch, F., & Guardiola, E. (2003). Lista de comprobación (checklist) abreviada para la evaluación de artículos de investigación biomédica básica. MedicinaClínica, 121(6), 228–230. https://doi.org/10.1016/s0025-7753(03)73913-x
Brikell, I., Kuja-Halkola, R., & Larsson, H. (2019). Heritability of attention-deficit hyperactivity disorder across the lifespan. European Neuropsychopharmacology, 29(3), S757-S758. https://doi.org/10.1016/j.euroneuro.2017.06.106
Burgos, N., & Colliot, O. (2020). Machine learning for classification and prediction of brain diseases: recent advances and upcoming challenges. Current Opinion in Neurology, 33(4), 439-450. https://doi.org/10.1097/WCO.0000000000000838
Cao, M., Martin, E., & Li, X. (2023). Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms. Translational Psychiatry, 13, Artículo 236. https://doi.org/10.1038/s41398-023-02536-w
*Cervantes-Henríquez, M. L., Acosta-López, J. E., Martinez, A. F., Arcos-Burgos, M., Puentes-Rozo, P. J., &Vélez, J. I. (2022). Machine Learning Prediction of ADHD Severity: Association and Linkage to ADGRL3, DRD4, and SNAP25. Journal of Attention Disorders, 26(4), 587–605. https://doi.org/10.1177/10870547211015426
*Chang, Y., Stevenson, C., Chen, I. C., Lin, D. S., & Ko, L. W. (2022). Neurological state changes indicative of ADHD in children learned via EEG-based LSTM networks. Journal of Neural Engineering, 19, Artículo016021. https://doi.org/10.1088/1741-2552/ac4f07
Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., Cohen, Z., Belgrave, D., DeRubeis, R., Iniesta, R., Dwyer, D. & Choi, K. (2021). The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry, 20(2), 154-170. https://doi.org/10.1002/wps.20882
Conejero, I., Jaussent, I., Lopez, R., Guillaume, S., Olié, E., Hebbache, C., Cohen, R.F., Kahn, J.P., Leboyer, M., Courtet, P., & Lopez-Castroman, J. (2019). Association of symptoms of attention deficit-hyperactivity disorder and impulsive-aggression with severity of suicidal behavior in adult attempters. Scientific Reports, 9, Artículo4593.https://doi.org/10.1038/s41598-019-41046-y
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 (22)00312-1
Dawson, A. E., Wymbs, B. T., Evans, S. W., & DuPaul, G. J. (2019). Exploring how adolescents with ADHD use and interact with technology. Journal of Adolescence, 71(1), 119-137.https://doi.org/10.1016/j.adolescence.2019.01.004
*Deshpande, G., Wang, P., Rangaprakash, D., & Wilamowski, B. (2015). Fully connected cascade artificial neural network architecture for attention deficit hyperactivity disorder classification from functional magnetic resonance imaging data. IEEE Transactions on Cybernetics, 45(12), 2668–2679. https://doi.org/10.1109/TCYB.2014.2379621
*Du, J., Wang, L., Jie, B., & Zhang, D. (2016). Network-based classification of ADHD patients using discriminative subnetwork selection and graph kernel PCA. Computerized Medical Imaging and Graphics, 52, 82–88. https://doi.org/10.1016/j.compmedimag.2016.04.004
*Duda, M., Haber, N., Daniels, J., & Wall, D. P. (2017). Crowdsourced validation of a machine-learning classification system for autism and ADHD. Translational Psychiatry, 7, Artículo e1133.https://doi.org/10.1038/tp.2017.86
*Faedda, G. L., Ohashi, K., Hernandez, M., McGreenery, C. E., Grant, M. C., Baroni, A., Polcari, A., & Teicher, M. H. (2016). Actigraph measures discriminate pediatric bipolar disorder from attention-deficit/hyperactivity disorder and typically developing controls. The Journal of Child Psychology and Psychiatry, 57(6), 706–716. https://doi.org/10.1111/jcpp.12520
Fayyad, J., Sampson, N. A., Hwang, I., Adamowski, T., Aguilar-Gaxiola, S., Al-Hamzawi, A., Andrade, L. H.G., Borges, G., de Girolamo, G., Florescu, S., Gureje, O., Haro, J. M., Hu, C., Karam, E. G., Lee, S., Navarro-Mateu, F., O’Neill, S., Pennell,B.E., Piazza, M., … Kessler, R. C. (2017). The descriptive epidemiology of DSM-IV adult ADHD in the world health organization world mental health surveys. ADHD Attention Deficit and Hyperactivity Disorders, 9, 47-65. https://doi.org/10.1007/s12402-016-0208-3
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 characteristic data to identify ADHD and autism. PLoS ONE, 11(12), Artículo e0166934. https://doi.org/10.1371/journal.pone.0166934
Ging-Jehli, N. R., Ratcliff, R., & Arnold, L. E. (2021). Improving neurocognitive testing using computational psychiatry—A systematic review for ADHD. Psychological Bulletin, 147(2), 169–231. https://doi.org/10.1037/bul0000319
*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 Impairment: The Potential of a Machine Learning Approach. Research on Child and Adolescent Psychopathology, 51, 679–691. https://doi.org/10.1007/s10802-023-01022-7
González, M. N., & Depaula, P. D. (2023). Parenting stress and coping strategies in mothers of children with Attention Deficit Hyperactivity Disorder in Argentina. Revista Argentina De Ciencias Del Comportamiento, 15(1), 84–92. https://doi.org/10.32348/1852.4206.v15.n1.33294
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, D., Oosterlaan, J., Faraone, S.V., Franke, B., Zwiers, M. P., Arias-Vasquez, A., & Buitelaar, J.K. (2015). Developmentally Stable Whole-Brain Volume Reductions and Developmentally Sensitive Caudate and Putamen Volume Alterations in Those with Attention-Deficit/Hyperactivity Disorder and Their Unaffected Siblings. JAMA Psychiatry, 72(5), 490-499. https://doi.org/10.1001/jamapsychiatry.2014.3162
Harkins, C. M., Handen, B. L., & Mazurek, M. O. (2022). The impact of the comorbidity of ASD and ADHD on social impairment. Journal of Autism and Developmental Disorders, 52, 2512-2522. https://doi.org/10.1007/s10803-021-05150-1
Harrison, A. G., & Edwards, M. J. (2023). The Ability of Self-Report Methods to Accurately Diagnose Attention Deficit Hyperactivity Disorder: A Systematic Review. Journal of Attention Disorders, 27(12), 1343-1359. https://doi.org/10.1177/10870547231177470
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, J.T., &Fair, D.A. (2020). Polygenic Risk Score–Derived Subcortical Connectivity Mediates Attention-Deficit/Hyperactivity Disorder Diagnosis. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 5(3), 330-341. https://doi.org/10.1016/j.bpsc.2019.11.014
Hoelzle, J. B., Ritchie, K. A., Marshall, P. S., Vogt, E. M., & Marra, D. E. (2019). Erroneous conclusions: The impact of failing to identify invalid symptom presentation when conducting adult attention-deficit/hyperactivity disorder (ADHD) research. Psychological Assessment, 31(9), 1174-1179.https://doi.org/10.1037/pas0000752
Holland, J., & Sayal, K. (2019). Relative age and ADHD symptoms, diagnosis and medication: a systematic review. European Child & Adolescent Psychiatry, 28, 1417-1429. https://doi.org/10.1007/s00787-018-1229-6
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 versions to assess ADHD symptoms and related problems. Clinical Child Psychology and Psychiatry, 24(4), 791-808. https://doi.org/10.1177/1359104519846602
*Jung, M., Mizuno, Y., Fujisawa, T. X., Takiguchi, S., Kong, J., Kosaka, H., & Tomoda, A. (2019). The Effects of COMT Polymorphism on Cortical Thickness and Surface Area Abnormalities in Children with ADHD. Cerebral Cortex, 29(9), 3902–3911. https://doi.org/10.1093/cercor/bhy269
*Jung, M., Tu, Y., Park, J., Jorgenson, K., Lang, C., Song, W., & Kong, J. (2019). Surface-based shared and distinct resting functional connectivity in attention-deficit hyperactivity disorder and autism spectrum disorder. The British Journal of Psychiatry, 214(6), 339–344. https://doi.org/10.1192/bjp.2018.248
Kelleher, J. D., Mac Namee, B., & D'arcy, A. (2020). Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT press.
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 Disorder and Sleep Problems with Wearable Data in Children. JAMA Network Open, 6(3), Artículo e233502. https://doi.org/10.1001/jamanetworkopen.2023.3502
*Kim, S., Lee, H.-K., & Lee, K. (2021). Screening of mood symptoms using MMPI-2-RF scales: An application of machine learning techniques. Journal of Personalized Medicine, 11(8), Artículo 812. https://doi.org/10.3390/jpm11080812
*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). Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals. Computers in Biology and Medicine, 140, Artículo 105120. https://doi.org/10.1016/j.compbiomed.2021.105120
Koppe, G., Meyer-Lindenberg, A., & Durstewitz, D. (2021). Deep learning for small and big data in psychiatry. Neuropsychopharmacology, 46, 176-190. https://doi.org/10.1038/s41386-020-0767-z
*Kuang, D., & He, L. (2014, November). Classification on ADHD with deep learning. En 2014 International Conference on Cloud Computing and Big Data (pp. 27-32). IEEE. https://doi.org/10.1109/CCBD.2014.42
*Lin, I. C., Chang, S. C., Huang, Y. J., Kuo, T. B. J., & Chiu, H. W. (2023). Distinguishing different types of attention deficit hyperactivity disorder in children using artificial neural network with clinical intelligent test. Frontiers in Psychology, 13, Artículo1067771. https://doi.org/10.3389/fpsyg.2022.1067771
Loh, H. W., Ooi, C. P., Barua, P. D., Palmer, E. E., Molinari, F., & Acharya, U. R. (2022). Automated detection of ADHD: Current trends and future perspective. Computers in Biology and Medicine, 146, Artículo105525.https://doi.org/10.1016/j.compbiomed.2022.105525
*Lohani, D. C., & Rana, B. (2023). ADHD Diagnosis using structural Brain MRI and Personal Characteristic Data with Machine Learning Framework. Psychiatry Research: Neuroimaging, 334, Artículo111689. https://doi.org/10.1016/j.pscychresns.2023.111689
Lugoboni, F., & Tinghino, B. (2022). Combined prevention for substance use and mental health problems in youth: A glance at two conditions at high risk for addiction. En M. Colizzi & M. Ruggeri (Eds.), Prevention in mental health: From risk management to early intervention (pp. 189-201). Springer International Publishing. https://doi.org/10.1007/978-3-030-97906-5_11
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 spectral power in children with ADHD and its subtypes. European Child & Adolescent Psychiatry, 32, 2223-2234. https://doi.org/10.1007/s00787-022-02068-6
Mariggió, M. A., Palumbi, R., Vinella, A., Laterza, R., Petruzzelli, M. G., Peschechera, A., Gabellone, A., Gentile, O., Vincenti, A., & Margari, L. (2021). DRD1 and DRD2 receptor polymorphisms: genetic neuromodulation of the dopaminergic system as a risk factor for ASD, ADHD and ASD/ADHD overlap. Frontiers in Neuroscience, 15, Artículo705890. https://doi.org/10.3389/fnins.2021.705890
Martel, M. M., Schimmack, U., Nikolas, M., &Nigg, J. T. (2015). Integration of symptom ratings from multiple informants in ADHD diagnosis: a psychometric model with clinical utility. Psychological Assessment, 27(3), 1060-1071. https://doi.org/10.1037/pas0000088
Martella, D., Aldunate, N., Fuentes, L.J., & Sánchez-Pérez, N. (2020).Arousal and Executive Alterations in Attention Deficit Hyperactivity Disorder (ADHD). Frontiers in Psychology, 11,Artículo1991.https://doi.org/10.3389/fpsyg.2020.01991
Mengi, M., & Malhotra, D. (2022). Artificial Intelligence Based Techniques for the Detection of Socio-Behavioral Disorders: A Systematic Review. Archives of Computational Methods in Engineering, 29, 2811–2855. https://doi.org/10.1007/s11831-021-09682-8
*Mikolas, P., Vahid, A., Bernardoni, F., Süß, M., Martini, J., Beste, C., & Bluschke, A. (2022). Training a machine learning classifier to identify ADHD based on real-world clinical data from medical records. Scientific Reports, 12, Artículo 12934. https://doi.org/10.1038/s41598-022-17126-x
*Mohammadi, M. R., Khaleghi, A., Nasrabadi, A. M., Rafieivand, S., Begol, M., & Zarafshan, H. (2016). EEG classification of ADHD and normal children using non-linear features and neural network. Biomedical Engineering Letters, 6, 66–73. https://doi.org/10.1007/s13534-016-0218-2
Mu, S., Wu, H., Zhang, J., & Chang, C. (2022). Structural brain changes and associated symptoms of ADHD subtypes in children. Cerebral Cortex, 32(6), 1152-1158. https://doi.org/10.1093/cercor/bhab276
*O’Mahony, N., Florentino-Liano, B., Carballo, J. J., Baca-García, E., & Rodríguez, A. A. (2014). Objective diagnosis of ADHD using IMUs. Medical Engineering and Physics, 36(7), 922–926. https://doi.org/10.1016/j.medengphy.2014.02.023
*Öztekin, I., Finlayson, M. A., Graziano, P. A., & Dick, A. S. (2021). Is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of ADHD in young children? A machine learning investigation. Developmental Cognitive Neuroscience, 49, Artículo100966. https://doi.org/10.1016/j.dcn.2021.100966
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, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S. …Moher, D. (2021). Declaración PRISMA 2020: una guía actualizada para la publicación de revisiones sistemáticas. Revista Española de Cardiología, 74(9), 790–799. https://doi.org/10.1016/j.recesp.2021.06.016
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 with ADHD. Journal of Attention Disorders, 25(4), 519-529.https://doi.org/10.1177/1087054718816157
Pereira-Sanchez, V., & Castellanos, F. X. (2021). Neuroimaging in attention-deficit/hyperactivity disorder. Current Opinion in Psychiatry, 34(2), 105-111. https://doi.org/10.1097/YCO.0000000000000669
Periyasamy, R., Vibashan, V. S., Varghese, G. T., & Aleem, M. A. (2021). Machine learning techniques for the diagnosis of attention-deficit/hyperactivity disorder from magnetic resonance imaging: a concise review. Neurology India, 69(6), 1518-1523.https://doi.org/10.4103/0028-3886.333520
Polanczyk, G. V., Willcutt, E. G., Salum, G. A., Kieling, C., & Rohde, L. A. (2014). ADHD prevalence estimates across three decades: an updated systematic review and meta-regression analysis. International Journal of Epidemiology, 43(2), 434-442.https://doi.org/10.1093/ije/dyt261
Jothi Prabha, A., & Bhargavi, R. (2019). Prediction of dyslexia using machine learning—a research travelogue. En V. Nath & J. Mandal (Eds.), Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems (pp. 23-34). Springer, Singapore. https://doi.org/10.1007/978-981-13-7091-5_3
Quintero-López, C., Gil-Vera, V. D., Landinez-Martínez, D. A., Vargas-Gaviria, J. P., & Gómez-Muñoz, N. (2023). Predictive Neurocognitive Model of Attention Deficit Hyperactivity Disorder Diagnosis. Mediterranean Journal of Clinical Psychology, 11(1), 1-25. https://doi.org/10.13129/2282-1619/mjcp-3606
*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 hierarchical feature extraction and extreme learning machine using structural and functional brain MRI. Frontiers in Human Neuroscience, 11, Artículo 157. https://doi.org/10.3389/fnhum.2017.00157
Scandurra, V., Emberti Gialloreti, L., Barbanera, F., Scordo, M. R., Pierini, A., & Canitano, R. (2019). Neurodevelopmental disorders and adaptive functions: a study of children with autism spectrum disorders (ASD) and/or attention deficit and hyperactivity disorder (ADHD). Frontiers in Psychiatry, 10, Artículo673. https://doi.org/10.3389/fpsyt.2019.00673
Silva, M., & Graña, M. (2022). On Machine Learning for Autism Prediction from Functional Connectivity. En M. Choraś, R. S. Choraś, M. Kurzyński, P. Trajdos, J. Pejaś, & T. Hyla, (Eds). Progress in Image Processing, Pattern Recognition and Communication Systems (pp. 163-172). Springer.https://doi.org/10.1007/978-3-030-81523-3_16
*Slobodin, O., Yahav, I., & Berger, I. (2020). A Machine-Based Prediction Model of ADHD Using CPT Data. Frontiers in Human Neuroscience, 14, Artículo560021. https://doi.org/10.3389/fnhum.2020.560021
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 aspect of behavior assessment system for children-2, parent rating scale-child: standardization in Korean children. Yonsei Medical Journal, 58(2), 439-448. https://doi.org/10.3349/ymj.2017.58.2.439
Song, C., Jiang, Z. Q., Liu, D., & Wu, L. L. (2022). Application and research progress of machine learning in the diagnosis and treatment of neurodevelopmental disorders in children. Frontiers in Psychiatry, 13, Artículo 960672. https://doi.org/10.3389/fpsyt.2022.960672
Sordo, S. Á., Garrido Hernansaiz, H., Cantero García, M., Sánchez Iglesias, I., González Moreno, J., & Santacreu, J. (2021). Validez de las pruebas de atención para el diagnóstico diferencial de TDAH infantil y Trastornos del Aprendizaje. Revista Electrónica de Investigación Psicoeducativa, 19(54), 437-464. https://doi.org/10.25115/ejrep.v19i54.3868
Sundaresan, A., Penchina, B., Cheong, S., Grace, V., Valero-Cabré, A., & Martel, A. (2021). Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI. Brain Informatics, 8(1), Artículo 13. https://doi.org/10.1186/s40708-021-00133-5
Rae, T., Sanders, S., Doust, J., Beller, E., & Glasziou, P. (2015). Prevalence of attention-deficit/hyperactivity disorder: a systematic review and meta-analysis. Pediatrics, 135(4), e994-e1001.https://doi.org/10.1542/peds.2014-3482
*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 detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals. Computer Methods and Programs in Biomedicine, 200, Artículo 105941. https://doi.org/10.1016/j.cmpb.2021.105941
*Vaidya, C. J., You, X., Mostofsky, S., Pereira, F., Berl, M. M., & Kenworthy, L. (2020). Data-driven identification of subtypes of executive function across typical development, attention deficit hyperactivity disorder, and autism spectrum disorders. The Journal of Child Psychology and Psychiatry, 61(1), 51–61. https://doi.org/10.1111/jcpp.13114
Vimalajeewa, D., McDonald, E., Bruce, S. A., & Vidakovic, B. (2022). Wavelet-based approach for diagnosing attention deficit hyperactivity disorder (ADHD). Scientific Reports, 12(1), Artículo 21928. https://doi.org/10.1038/s41598-022-26077-2
Wang, M., & Chen, H. (2020). Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Applied Soft Computing, 88, Artículo105946. https://doi.org/10.1016/j.asoc.2019.105946
Weiss, F., Tidona, S., Carli, M., Perugi, G., & Scarselli, M. (2023). Triple Diagnosis of Attention-Deficit/Hyperactivity Disorder with Coexisting Bipolar and Alcohol Use Disorders: Clinical Aspects and Pharmacological Treatments. Current Neuropharmacology, 21(7), 1467-1476.https://doi.org/10.2174/1570159X20666220830154002
Whitney, D. G., & Peterson, M. D. (2019). US national and state-level prevalence of mental health disorders and disparities of mental health care use in children. JAMA Pediatrics, 173(4), 389-391. https://doi.org/10.1001/jamapediatrics.2018.5399
Yang, S., Ma, W., Pi, X., & Qian, S. (2019). A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources. Transportation Research Part C: Emerging Technologies, 107, 248-265. https://doi.org/10.1016/j.trc.2019.08.010
Yasumura, A., Omori, M., Fukuda, A., Takahashi, J., Yasumura, Y., Nakagawa, E., Koike, T., Yamashita, Y., Miyajima, T., Koeda, T., Aihara, M., Tachimori, H., & Inagaki, M. (2020). Applied Machine Learning Method to Predict Children with ADHD Using Prefrontal Cortex Activity: A Multicenter Study in Japan. Journal of Attention Disorders, 24(14), 2012–2020. https://doi.org/10.1177/1087054717740632
*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 Neuro-Behavior Sensing for Attention-Deficit/Hyperactivity Disorder Intelligent Assessment. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(9), 1899–1907. https://doi.org/10.1109/TNSRE.2020.3004545
*Yoo, J. H., Kim, J. I., Kim, B. N., & Jeong, B. (2020). Exploring characteristic features of attention-deficit/hyperactivity disorder: findings from multi-modal MRI and candidate genetic data. Brain Imaging and Behavior, 14, 2132–2147. https://doi.org/10.1007/s11682-019-00164-x
Young, S., Adamo, N., Ásgeirsdóttir, B. B., Branney, P., Beckett, M., Colley, W., Cubbim, S., Deeley, Q., Farrag, E., Gudjonsson, G., Hill, P., Hollingdale, J., Kilic, O., Lloyd, T., Mason, P., Paliokosta, E., Perecherla, S., Sedgwick, J., Skirrow, C. … Woodhouse, E. (2020). Females with ADHD: An expert consensus statement taking a lifespan approach providing guidance for the identification and treatment of attention-deficit/hyperactivity disorder in girls and women. BMC Psychiatry, 20, Artículo 404.https://doi.org/10.1186/s12888-020-02707-9
*Zhang-James, Y., Chen, Q., Kuja-Halkola, R., Lichtenstein, P., Larsson, H., & Faraone, S. V. (2020). Machine-Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data. The Journal of Child Psychology and Psychiatry, 61(12), 1370–1379.https://doi.org/10.1111/jcpp.13226
Zhang-James, Y., Razavi, A. S., Hoogman, M., Franke, B., &Faraone, S. V. (2023). Machine learning and MRI-based diagnostic models for ADHD: Are we there yet? JournalofAttentionDisorders, 27(4), 335-353.https://doi.org/10.1177/10870547221146256