Top

Journal of Autism and Developmental Disorders

Gepubliceerd in:

28-06-2022 | Brief Report

Brief Report: Machine Learning for Estimating Prognosis of Children with Autism Receiving Early Behavioral Intervention—A Proof of Concept

Auteurs: Isabelle Préfontaine, Marc J. Lanovaz, Mélina Rivard

Gepubliceerd in: Journal of Autism and Developmental Disorders | Uitgave 4/2024

Abstract

Although early behavioral intervention is considered as empirically-supported for children with autism, estimating treatment prognosis is a challenge for practitioners. One potential solution is to use machine learning to guide the prediction of the response to intervention. Thus, our study compared five machine algorithms in estimating treatment prognosis on two outcomes (i.e., adaptive functioning and autistic symptoms) in children with autism receiving early behavioral intervention in a community setting. Each machine learning algorithm produced better predictions than random sampling on both outcomes. Those results indicate that machine learning is a promising approach to estimating prognosis in children with autism, but studies comparing these predictions with those produced by qualified practitioners remain necessary.

vorige artikel Age- and Gender-Specific Prevalence of Intellectually Disabled Population in India

volgende artikel A multi-perspective study of Perceived Inclusive Education for students with Neurodevelopmental Disorders

Because the intensity of the intervention provided to our sample may not qualify the intervention as being “intensive”, we will use the expression early behavioral intervention when referring to the program.

Special education technicians is a terminology unique to the province of Quebec and refers to college-level technicians.

Bieleninik, È., Posserud, M. B., Geretsegger, M., Thompson, G., Elefant, C., & Gold, C. (2017). Tracing the temporal stability of autism spectrum diagnosis and severity as measured by the autism diagnostic observation schedule: A systematic review and meta-analysis. Plos One, 12(9), e0183160. https://doi.org/10.1371/journal.pone.0183160CrossRefPubMedPubMedCentral

Cearns, M., Hahn, T., & Baune, B. T. (2019). Recommendations and future directions for supervised machine learning in psychiatry. Translational Psychiatry, 9(1), 271. https://doi.org/10.1038/s41398-019-0607-2CrossRefPubMedPubMedCentral

Chekroud, A. M., Zotti, R. J., Shehzad, Z., et al. (2016). Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry, 3(3), 243–250. https://doi.org/10.1016/S2215-0366(15)00471-XCrossRefPubMed

Daemi, A., Kodamara, H., & Huang, B. (2019). Gaussian process modelling with Gaussian mixture likelihood. Journal of Process Control, 81, 209–220. https://doi.org/10.1016/j.jprocont.2019.06.007CrossRef

Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018). Machine learning approaches for clinical psychology and psychiatry. Annual Review of Clinical Psychology, 14(1), 91–118. https://doi.org/10.1146/annurev-clinpsy-032816-045037CrossRefPubMed

Eapen, V., Crncec, R., & Walter, A. (2013). Exploring Links between genotypes, phenotypes, and clinical predictors of response to early intensive behavioral intervention in autism spectrum disorder. Frontiers in Human Neuroscience, 7, 567. https://doi.org/10.3389/fnhum.2013.00567CrossRefPubMedPubMedCentral

Eldevik, S., Hastings, R. P., Hughes, J. C., Jahr, E., Eikeseth, S., & Cross, S. (2010). Using participant data to extend the evidence base for intensive behavioral intervention for children with autism. American Journal on Intellectual and Developmental Disabilities, 115(5), 381–405. https://doi.org/10.1352/1944-7558-115.5.381CrossRefPubMed

Fava, L., & Strauss, K. (2014). Response to early intensive behavioral intervention for autism—An umbrella approach to issues critical to treatment individualization. International Journal of Developmental Neuroscience: The Official Journal of the International Society for Developmental Neuroscience, 39, 49–58. https://doi.org/10.1016/j.ijdevneu.2014.05.004CrossRefPubMed

Frazier, T. W., Georgiades, S., Bishop, S. L., & Hardan, A. Y. (2014). Behavioral and cognitive characteristics of females and males with autism in the Simons Simplex Collection. Journal of the American Academy of Child and Adolescent Psychiatry, 53(3), 329–40e403. https://doi.org/10.1016/j.jaac.2013.12.004CrossRefPubMed

Gabriels, R. L., Hill, D. E., Pierce, R. A., Rogers, S. J., & Wehner, B. (2001). Predictors of treatment outcome in young children with autism: A retrospective study. Autism, 5(4), 407–429. https://doi.org/10.1177/1362361301005004006CrossRefPubMed

Harrison, P. L., & Oakland, T. (2003). Adaptive Behavior Assessment System (2nd ed.). San Antonio, TX: The Psychological Corporation

Howlin, P., Magiati, I., & Charman, T. (2009). Systematic review of early intensive behavioral interventions for children with autism. American Journal on Intellectual and Developmental Disabilities, 114(1), 23–41. https://doi.org/10.1352/2009.114:23-41CrossRefPubMed

Hull, L., Mandy, W., & Petrides, K. V. (2017). Behavioural and cognitive sex/gender differences in autism spectrum condition and typically developing males and females. Autism: The International Journal of Research and Practice, 21(6), 706–727. https://doi.org/10.1177/1362361316669087CrossRefPubMed

Jiang, T., Gradus, J. L., & Rossellini, A. J. (2020). Supervised machine learning: A brief primer. Behavior Therapy, 51(5), 675–687. https://doi.org/10.1016/j.beth.2020.05.002CrossRefPubMedPubMedCentral

Lovaas, O. I. (1981). Teaching developmentally disabled children: The ME book. Austin, TX: Pro-Ed

Magiati, I., Moss, J., Charman, T., & Howlin, P. (2011). Patterns of change in children with autism spectrum disorders who received community based comprehensive interventions in their pre-school years: A seven year follow-up study. Research in Autism Spectrum Disorders, 5(3), 1016–1027. https://doi.org/10.1016/j.rasd.2010.11.007CrossRef

Makrygianni, M. K., Gena, A., Katoudi, S., & Galanis, P. (2018). The effectiveness of applied behavior analytic interventions for children with autism spectrum disorder: A meta-analytic study. Research in Autism Spectrum Disorders, 51, 18–31. https://doi.org/10.1016/j.rasd.2018.03.006CrossRef

Makrygianni, M. K., & Reed, P. (2010). A meta-analytic review of the effectiveness of behavioural early intervention programs for children with autistic spectrum disorders. Research in Autism Spectrum Disorders, 4(4), 577–593. https://doi.org/10.1016/j.rasd.2010.01.014CrossRef

Maurice, C., Green, G., & Luce, S. (1996). Behavioral intervention for young children with autism: A manual for parents and professionals. Austin, TX: Pro-Ed

Miotto, R., Wang, F., Wang, S., Jiang, X., & et Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics, 19(6), 1236–1246. doi:https://doi.org/10.1093/bib/bbx044CrossRefPubMed

Parikh, M. N., Li, H., & He, L. (2019). Enhancing diagnosis of autism with optimized machine learning models and personal characteristic data. Frontiers in Computational Neuroscience, 13, 9. https://doi.org/10.3389/fncom.2019.00009CrossRefPubMedPubMedCentral

Préfontaine, I., Morizot, J., Lanovaz, M. J., & Rivard, M. (2021). Changes in autistic symptoms and adaptive functioning of children receiving early behavioral intervention in a community setting: A latent growth curve analysis. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-021-05373-2CrossRefPubMed

Reed, P. (2016). Interventions for autism: Evidence for educational and clinical practice. Hoboken: WileyCrossRef

Reichow, B., Hume, K., Barton, E. E., & Boyd, B. A. (2018). Early intensive behavioral intervention (EIBI) for young children with autism spectrum disorders (ASD). The Cochrane Database of Systematic Reviews, 5, CD009260. https://doi.org/10.1002/14651858.CD009260.pub3CrossRefPubMed

Rivard, M., Terroux, A., & Mercier, C. (2014). Effectiveness of early behavioral intervention in public and mainstream settings: The case of preschool-age children with autism spectrum disorders. Research in Autism Spectrum Disorders, 8, 1031–1043. doi: https://doi.org/10.1016/j.rasd.2014.05.010CrossRef

Rivard, M., Morin, M., Mello, C., Terroux, A., & Mercier, C. (2019). Follow-up of children with autism spectrum disorder 1 year after early behavioral intervention. Behavior Modification, 43(4), 490–517. https://doi.org/10.1177/0145445518773692CrossRefPubMed

Sallow, G. O., & Graupner, T. D. (2002). Intensive behavioral treatment for children with autism: Four-year outcome and predictors. American Journal on Mental Retardation, 110(6), 417–438. https://doi.org/10.1352/0895-8017(2005)110[417:IBTFCW]2.0.CO;2CrossRef

Schopler, E., et, & Van Bourgondien, M. E. (2010). Childhood Autism Rating Scale, Second Edition (CARS-2). Los Angeles, CA: Western Psychological Services

Sengupta, P. P., & et Shrestha, S. (2019). Machine learning for data-driven discovery. Journals of the American College of Cardiology: Cardiovascular Imaging, 12(4), 690–692. https://doi.org/10.1016/j.jcmg.2018.06.030CrossRef

Shatte, A. B. R., Hutchinson, D. M. et, & Teague, S. (2020). Machine learning in mental health: a scoping review of methods and applications. Psychological Medicine, 49, 1426–1448. https://doi.org/10.1017/S0033CrossRef

Singh, A., Thakur, N., & Sharma, A. (2016). A review of supervised machine learning algorithms. In 2016 3rd international conference on computing for sustainable global development (pp. 1310–1315). IEEE

Smith, T., Klorman, R., & Mruzek, D. W. (2015). Predicting outcome of community-based early intensive behavioral intervention for children with autism. Journal of Abnormal Child Psychology, 43(7), 1271–1282. https://doi.org/10.1007/s10802-015-0002-2CrossRefPubMed

Tiura, M., Kim, J., Detmers, D., & Baldi, H. (2017). Predictors of longitudinal ABA treatment outcomes for children with autism: A growth curve analysis. Research in Developmental Disabilities,70(Supplement C), 185–197. doi: https://doi.org/10.1016/j.ridd.2017.09.008

Turgeon, S., & Lanovaz, M. J. (2020). Tutorial: Applying machine learning in behavioral research. Perspectives on Behavior Science, 43(4), 697–723. https://doi.org/10.1007/s40614-020-00270-yCrossRefPubMedPubMedCentral

Vivanti, G., Prior, M., Williams, K., & Dissanayake, C. (2014). Predictors of outcomes in autism early intervention: Why don’t we know more? Frontiers in Pediatrics, 2, 58. https://doi.org/10.3389/fped.2014.00058CrossRefPubMedPubMedCentral

Warren, Z., McPheeters, M. L., Sathe, N., Foss-Feig, J. H., Glasser, A., & Veenstra-VanderWeele, J. (2011). A systematic review of early intensive intervention for autism spectrum disorders. Pediatrics, 127(5), e1303. https://doi.org/10.1542/peds.2011-0426CrossRefPubMed

Wechsler, D. (2003). The Wechsler preschool and primary scale of intelligence administration and scoring manual (3rd ed.).). Harcourt Assessment: The Psychological Corporation, London

Yang, L., & Shami, A. (2020). On hyperparameter optimization of machine learning algorithms: Theory to practice. Neurocomputing, 415(20), 294–316. https://doi.org/10.1016/j.neucom.2020.07.061CrossRef

Yarkoni, T., & et Westfall, J. (2017). Choosing prediction over explanation in psychology: lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122. https://doi.org/10.1177/1745691617693393CrossRefPubMedPubMedCentral

Titel: Brief Report: Machine Learning for Estimating Prognosis of Children with Autism Receiving Early Behavioral Intervention—A Proof of Concept
Auteurs: Isabelle Préfontaine
Marc J. Lanovaz
Mélina Rivard
Publicatiedatum: 28-06-2022
Uitgeverij: Springer US
Gepubliceerd in: Journal of Autism and Developmental Disorders / Uitgave 4/2024
Print ISSN: 0162-3257
Elektronisch ISSN: 1573-3432
DOI: https://doi.org/10.1007/s10803-022-05641-9

Bohn Stafleu van Loghum

Deel dit onderdeel of sectie (kopieer de link)

Abstract

Log in om toegang te krijgen

Andere artikelen Uitgave 4/2024

Virtual Training of Medical Students to Promote the Comfort and Cooperation of Patients with Neurodevelopmental Disabilities

Age- and Gender-Specific Prevalence of Intellectually Disabled Population in India

Extreme Learning Machine (ELM) Method for Classification of Preschool Children Brain Imaging

Estimating the Prevalence of Autism Spectrum Disorder in New South Wales, Australia: A Data Linkage Study of Three Routinely Collected Datasets

Correction: Predictors of Access to Early Support in Families of Children with Suspected or Diagnosed Developmental Disabilities in the United Kingdom

Integrating Big Data and Assistive Technology to Improve College Students’ Public Mental Health Quality During the COVID-19 Pandemic