Autism spectrum disorder and artificial intelligence applications

Year 2020, Volume: 6 Issue: 3, 92 - 111, 30.07.2020

Zekeriya Alperen Sağdıç , Sunagül Sani-bozkurt

Abstract

In this study, it is aimed to give information about the reflections of artificial intelligence technologies and to make relevant suggestions by examining the related literature regarding the use of artificial intelligence technologies in the field of Autism Spectrum Disorder (ASD). Considering the research articles involving artificial intelligence applications with individuals with ASD; It is seen that these studies are applications for diagnosing the special needs of individuals with ASD and for the intervention applied to increase the quality of education. Also, it is remarkable that limited number of research has been carried out in the context of repetitive and obsessive behavior, interest and effectiveness patterns, and social interaction and communication deficiencies, which is one of the most important diagnostic criteria of ASD. Although the results of studies on ASD and artificial intelligence are promising, it is one of the issues that should be further emphasized that what kind of results this technology may cause for children with ASD in the future. For this purpose, it is suggested that future studies concerning individuals with ASD should be carried out to expand and support the findings in the literature.

Keywords

Artificial intelligence, machine learning, deep learning, expert systems, autism spectrum disorder

Otizm spektrum bozukluğu ve yapay zeka uygulamaları

Abstract

Bu çalışmada yapay zeka teknolojilerinin Otizm Spektrum Bozukluğu (OSB) alanında kullanımının incelenmesine yönelik ilgili alanyazın incelenerek yapay zeka teknolojilerinin OSB alanındaki yansımaları hakkında bilgi vermek ve ilgili önerilerde bulunmak amaçlanmıştır. OSB olan bireylerle yapılan yapay zeka uygulamalarını içeren araştırmalar ele alındığında; sıklıkla bu araştırmaların OSB olan bireyin ihtiyaç duyduğu özel gereksinimi tanılamaya ve eğitim kalitesini arttırmak amaçlı uygulanan müdahaleye yönelik uygulamalar olduğu görülmektedir. Ayrıca OSB’nin en önemli tanı ölçütlerinden biri olan sosyal etkileşim ve iletişimdeki yetersizlikleri ile sınırlı, yineleyici ve takıntılı davranış, ilgi ve etkinlik örüntüleri bağlamında oldukça sınırlı araştırmalar gerçekleştirildiği dikkat çekmektedir. OSB ve yapay zeka ilgili olarak yapılan çalışmaların sonuçları ümit verici olmakla birlikte bu teknolojinin OSB olan çocuklar için ileride ne tür sonuçlara yol açabileceği üzerinde durulması gereken konulardan biri olmaktadır. Bunun için alanyazındaki bulguların genişletilmesi ve desteklenmesi amacıyla OSB olan bireylere çeşitli alanlarda yapay zekanın kullanılmasına yönelik ileri araştırmaların yapılması önerilmektedir.

Keywords

Yapay zeka, makine öğrenmesi, derin öğrenme, uzman sistemler, otizm spektrum bozukluğu

References

• Abidi, S. S. R., & Manickam, S. (2002). Leveraging XML-based electronic medical records to extract experiential clinical knowledge. International Journal of Medical Informatics, 68(1-3), 187-203 https://doi.org/10.1016/S1386-5056(02)00076-X
• Afgan, N. H., & Carvalho, M. G. (1996). Knowledge-based expert system for fouling assessment of industrial heat exchangers. Applied Thermal Engineering, 16(3), 203-208. https://doi.org/10.1016/1359-4311(95)00001-1 Allahverdi, N. (2002). Uzman Sistemler: Bir yapay zeka Uygulaması. İstanbul: Atlas Yayın Dağıtım.
• Allison, C., Auyeung, B., & Baron-Cohen, S. (2012). Toward brief “red flags” for autism screening: the short autism spectrum quotient and the short quantitative checklist in 1,000 cases and 3,000 controls. Journal of the American Academy of Child & Adolescent Psychiatry, 51(2), 202-212. https://doi.org/10.1016/j.jaac.2011.11.003
• Alonso-Amo, F., Perez, A. G., Gomez, G. L., & Montes, C. (1995). An expert system for homeopathic glaucoma treatment (SEHO). Expert Systems with Applications, 8(1), 89-99. https://doi.org/10.1016/S0957-4174(94)E0001-B
• Alpaydin, E. (2016). Machine learning: the new AI. Cambridge: MIT press.
• Amiri, A. M., Peltier, N., Goldberg, C., Sun, Y., Nathan, A., Hiremath, S. V., & Mankodiya, K. (2017, March). WearSense: Detecting autism stereotypic behaviors through smartwatches. In Healthcare, 5(11), 2-9. https://doi.org/10.3390/healthcare5010011
• APA [American Psychiatric Association]. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing.
• Ataseven, B. (2013). Yapay sinir ağları ile öngörü modellemesi. Öneri Dergisi, 10(39), 101-115.
• Baumeister, R. F., & Leary, M. R. (1997). Writing narrative literature reviews. Review of General Psychology, 1(3), 311-320. https://doi.org/10.1037/1089-2680.1.3.311
• Bellman, R. (1978). An introduction to artificial intelligence: Can computers think?. Thomson Course Technology. Boyd & Fraser Publishing Company.
• Bhat, A. N., Galloway, J. C., & Landa, R. J. (2010). Social and non‐social visual attention patterns and associative learning in infants at risk for autism. Journal of Child Psychology and Psychiatry, 51(9), 989-997. https://doi.org/10.1111/j.1469-7610.2010.02262.x
• Brank, J., Grobelnik, M., & Mladenić, D. (2007). Automatic evaluation of ontologies. Kao, A., & Poteet, S. R. (Eds.). Natural language processing and text Mining içinde (pp. 193-219). London: Springer Science & Business Media.
• Chen, C. M. (2008). Intelligent web-based learning system with personalized learning path guidance. Computers and Education, 51(2), 787-814. https://doi.org/10.1016/j.compedu.2007.08.004
• D’Mello, S. K. (2016). Giving eyesight to the blind: Towards attention-aware AIED. International Journal of Artificial Intelligence in Education, 26(2), 645-659. https://doi.org/10.1007/s40593-016-0104-1
• Daniel, G. (2013). Principles of artificial neural networks. London: World Scientific.
• Dawood, N. N. (1996). A strategy of knowledge elicitation for developing an integrated bidding/production management expert system for the precast industry. Advances in Engineering Software, 25(2-3), 225-234.
• Deng, L., & Liu, Y. (2018). A joint introduction to natural language processing and to deep learning. Deng, L., & Liu, Y. (Eds.), Deep learning in natural language processing içinde (pp. 1-22). Singapore: Springer. https://doi.org/10.1007/978-981-10-5209-5
• Deshmukh, A., & Talluru, L. (1998). A rule-based fuzzy reasoning system for assessing the risk of management fraud. International Journal of Intelligent Systems in Accounting, Finance and Management, 7(4), 223-241.
• Donnelly, P. J., Blanchard, N., Samei, B., et al. (2016). Automatic teacher modeling from live classroom audio. In Proceedings of 24th ACM International Conference on User Modeling, Adaptation, and Personalization (UMAP) (pp. 45–53). New York: ACM.
• Drigas, A. S., & Ioannidou, R. E. (2012). Artificial Intelligence in Special Education: A decade review. International Journal of Engineering Education, 28(6), 1366-1373.
• Drigas, A. S., & Ioannidou, R. E. (2013). Special education and ICTs. International Journal of Emerging Technologies in Learning, 8(2), 41-47. http://dx.doi.org/10.3991/ijet.v8i2.2514
• Duda, M., Daniels, J., & Wall, D. P. (2016). Clinical evaluation of a novel and mobile autism risk assessment. Journal of Autism and Developmental Disorders, 46(6), 1953-1961. https://doi.org/10.1007/s10803-016-2718-4
• Eicher, B., Polepeddi, L., & Goel, A. (2018). Jill Watson Doesn't Care if You're Pregnant: Grounding AI Ethics in Empirical Studies. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (pp. 88-94). https://doi.org/10.1145/3278721.3278760
• Fu, Y., & Shen, R. (2004). GA based CBR approach in Q & A system. Expert Systems with Applications, 26(2), 167-170. https://doi.org/10.1016/S0957-4174(03)00117-9
• Goksel, N., & Bozkurt, A. (2019). Artificial intelligence in education: Current insights and future perspectives. In Handbook of Research on Learning in the Age of Transhumanism (pp. 224-236). Hershey: PA: IGI Global. https://doi.org/10.4018/978-1-5225-8431-5.ch014
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. London: MIT press.
• Graham-Jones, P. J., & Mellor, B. G. (1995). Expert and knowledge-based systems in failure analysis. Engineering Failure Analysis, 2(2), 137-149.
• Hamzaçebi, C., & Kutay, F. (2004). Yapay sinir ağları ile Türkiye elektrik enerjisi tüketiminin 2010 yılına kadar Tahmini. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 19(3), 227-233.
• Haugeland, J., (1985). Artificial intelligence: The very idea. Cambridge: The MIT Press.
• IEEE Standards Association (2019) The IEEE global initiative on ethics of autonomous and intelligent systems. Available at: https://standards.ieee.org/industry-connections/ec/autonomoussystems.html
• Knox, J., Wang, Y., & Gallagher, M. (2019). Artificial intelligence and inclusive education. Singapore: Springer.
• Kosmicki, J. A., Sochat, V., Duda, M., & Wall, D. P. (2015). Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning. Translational Psychiatry, 5(2), e514-e514. https://doi.org/10.1038/tp.2015.7
• Kurzweil, R. (2005). The singularity is near: When humans transcend biology. London: Penguin Books. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
• Liao, S. H. (2001). A knowledge-based architecture for implementing military geographical intelligence system on Intranet. Expert Systems with Applications, 20(4), 313-324. https://doi.org/10.1016/S0957-4174(01)00016-1
• Liao, S. H. (2005). Expert system methodologies and applications-a decade review from 1995 to 2004. Expert Systems with Applications, 28(1), 93-103. https://doi.org/10.1016/j.eswa.2004.08.003
• Lopez, M. A. A., Flores, C. H., & Garcia, E. G. (2003). An intelligent tutoring system for turbine startup training of electrical power plant operators. Expert Systems with Applications, 24(1), 95-101. https://doi.org/10.1016/S0957-4174(02)00087-8
• Maenner, M. J. (2020). Prevalence of autism spectrum disorder among children sged 8 years-autism and developmental disabilities monitoring network, 11 Sites, United States, 2016. MMWR. Surveillance Summaries, 69(4),1-12. https://doi.org/10.15585/mmwr.ss6904a1
• Mahaman, B. D., Passam, H. C., Sideridis, A. B., & Yialouris, C. P. (2003). DIARES-IPM: A diagnostic advisory rule-based expert system for integrated pest management in Solanaceous crop systems. Agricultural Systems, 76(3), 1119-1135 https://doi.org/10.1016/S0308-521X(02)00187-7
• Mohri, M., Rastomizadeh, A., & Talwalkar, A. (2012). Foundations of machine learning. London, England: The MIT Press.
• Mujeeb, S., Javed, M. H., & Arshad, T. (2017). Aquabot: a diagnostic chatbot for achluophobia and autism. International Journal of Advanced Computer Science and Applications, 8(9), 209-216. https://doi.org/10.14569/IJACSA.2017.080930
• Plant, R. E., & Vayssieres, M. P. (2000). Combining expert system and GIS technology to implement a state-transition model of oak woodlands. Computers and Electronics in Agriculture, 27(1-3) 71-93. https://doi.org/10.1016/S0168-1699(00)00099-5
• Rad, N. M., & Furlanello, C. (2016). Applying deep learning to stereotypical motor movement detection in autism spectrum disorders. In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) (pp. 1235-1242). IEEE.
• Riedl, M., Arriaga, R., Boujarwah, F., Hong, H., Isbell, J., & Heflin, J. (2009, October). Graphical social scenarios: Toward intervention and authoring for adolescents with high functioning autism. In 2009 AAAI Fall Symposium Series (pp. 64-73).
• Sabourin, L., & Villeneuve, F. (1996). OMEGA, an expert CAPP system. Advances in Engineering Software, 25(1), 51-59.
• Sacrey, L. A. R., Zwaigenbaum, L., Bryson, S., Brian, J., Smith, I. M., Roberts, W., ... & Garon, N. (2018). Parent and clinician agreement regarding early behavioral signs in 12‐and 18‐month‐old infants at‐risk of autism spectrum disorder. Autism Research, 11(3), 539-547. https://doi.org/10.1002/aur.1920
• Sharma, R. C., Kawachi, P., & Bozkurt, A. (2019). The landscape of artificial intelligence in open, online and distance education: Promises and Concerns. Asian Journal of Distance Education, 14(2),1-2. https://doi.org/10.5281/zenodo.3730631
• Sijing, L., & Lan, W. (2018, August). Artificial Intelligence Education Ethical Problems and Solutions. In 2018 13th International Conference on Computer Science & Education (ICCSE) (pp. 1-5). IEEE.
• Smola, A., & Vishwanathan, S. V. N. (2008). Introduction to machine learning. Cambridge: Cambridge University Press.
• Smutny, P., & Schreiberova, P. (2020). Chatbots for learning: A review of educational chatbots for the Facebook Messenger. Computers & Education, 151, 103862. https://doi.org/10.1016/j.compedu.2020.103862
• Srinivasan, B., & Parthasarathi, R. (2013). An intelligent task analysis approach for special education based on MIRA. Journal of Applied Logic, 11(1), 137-145. https://doi.org/10.1016/j.jal.2012.12.001
• Teigens, V. (2019). Yapay genel zeka (Çev. C.S.B. Equipment). Cambridge Stanford Books.
• Turban, E., & Aronson, J. E. (2001). Decision support systems and intelligent systems (6th ed.). Hong Kong: Prentice International Hall.
• Urwin, R. (2016). Artificial Intelligence The quest of the ultimate thinking machine. London: Publisher Arcturus Holdings Limited.
• Winston, P. H. (1992). Artificial Intelligence (3th. ed.). Boston: Addison-Wesley.
• Xue, M., & Zhu, C. (2009, April). A study and application on machine learning of artificial intellligence. In 2009 International Joint Conference on Artificial Intelligence (pp. 272-274). IEEE. https://doi.org/10.1109/JCAI.2009.55
• Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent trends in deep learning based natural language processing.IEEE Computational Intelligence Magazine, 13(3), 55-75. https://doi.org/10.1109/MCI.2018.2840738
• Yuan, J., Holtz, C., Smith, T., & Luo, J. (2017). Autism spectrum disorder detection from semi-structured and unstructured medical data. EURASIP Journal on Bioinformatics and Systems Biology, 1(3), 2-9. https://doi.org/10.1186/s13637-017-0057-1