| dc.contributor.author | Puerto, E. | |
| dc.contributor.author | Aguilar, J. | |
| dc.contributor.author | López, C. | |
| dc.contributor.author | Chávez, D. | |
| dc.date.accessioned | 2021-12-02T14:02:17Z | |
| dc.date.available | 2021-12-02T14:02:17Z | |
| dc.date.issued | 2019-02 | |
| dc.identifier.issn | 1568-4946 | |
| dc.identifier.uri | http://repositorio.ufps.edu.co/handle/ufps/1643 | |
| dc.description.abstract | Autism Spectrum Disorder (ASD) is comprised of a group of heterogeneous neurodevelopmental conditions, typically characterized by a triad of symptoms consisting of (1) impaired communication, (2) restricted interests, and (3) repetitive and stereotypical behavior pattern. An accurate and early diagnosis of autism can provide the basis for an appropriate educational and treatment program. In this work, we propose a computational model using a Multilayer Fuzzy Cognitive Map (hereafter referred to as MFCM) based on standardized behavioral assessments diagnosing the ASD (MFCM-ASD). The two standards used in the model are: the Autism Diagnostic Observation Schedule, Second Edition (ADOS2), and the Autism Diagnostic Interview Revised (ADIR). The MFCM’s are a soft computing technique characterized by robust properties that make it an effective technique for medical decision support systems. For the evaluation of the MFCM-ASD model, we have used real datasets of diagnosed cases, so as to compare against other method/approaches. Initial experiments demonstrated that the proposed model outperforms conventional Fuzzy Cognitive Maps (FCMs) for ASD diagnosis. Our MFCM-ASD model serves as a diagnostic tool required to support the medical decisions when determining the correct diagnosis of Autism in children with different cognitive characteristics. | eng |
| dc.format.extent | 42 páginas | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.language.iso | eng | spa |
| dc.publisher | Applied Soft Computing | spa |
| dc.relation.ispartof | Applied Soft Computing | |
| dc.rights | © 2018 Published by Elsevier B.V. | eng |
| dc.source | https://www.sciencedirect.com/science/article/abs/pii/S156849461830591X | spa |
| dc.title | Using multilayer fuzzy cognitive maps to diagnose autism spectrum disorder | eng |
| dc.type | Artículo de revista | spa |
| dcterms.references | American Psychiatric Association, “American Psychiatric Association. Diagnostic and statistical manual of mental disorders,” Washingt. DC, 2013. | spa |
| dcterms.references | M. Fakhoury, “Autistic spectrum disorders: A review of clinical features, theories and diagnosis,” International Journal of Developmental Neuroscience, vol. 43. pp. 70–77, 2015. | spa |
| dcterms.references | J. J. Willsey and M. W. State, “Autism spectrum disorders: From genes to neurobiology,” Current Opinion in Neurobiology, vol. 30. pp. 92–99, 2015. | spa |
| dcterms.references | R. H. Wozniak, N. B. Leezenbaum, J. B. Northrup, K. L. West, and J. M. Iverson, “The development of autism spectrum disorders: variability and causal complexity,” Wiley Interdisciplinary Reviews: Cognitive Science, vol. 8, no. 1–2. 2017. | spa |
| dcterms.references | C. Ecker, “The neuroanatomy of autism spectrum disorder: An overview of structural neuroimaging findings and their translatability to the clinical setting,” Autism, vol. 21, no. 1, pp. 18–28, 2017. | spa |
| dcterms.references | P. P. Groumpos, “Fuzzy Cognitive Maps: Basic theories and their application to complex systems,” Fuzzy Cogn. Maps, vol. 247, pp. 1–22, 2010. | spa |
| dcterms.references | C. D. Groumpos, V. C. Georgopoulos, G. A. Malandraki, and S. Chouliara, “Fuzzy cognitive map architectures for medical decision support systems,” Appl. Soft Comput., vol. 8, no. 3, pp. 1243–1251, 2008. | spa |
| dcterms.references | W. Froelich, “Towards improving the efficiency of the fuzzy cognitive map classifier,” Neurocomputing, vol. 232, pp. 83–93, 2017. | spa |
| dcterms.references | J. Aguilar, “A Fuzzy Cognitive Map Based on the Random Neural Model,” in Engineering of Intelligent Systems, vol. 2070, 2001, pp. 333–338. | spa |
| dcterms.references | V. C. Georgopoulos, G. A. Malandraki, and C. D. Stylios, “A fuzzy cognitive map approach to differential diagnosis of specific language impairment,” Artif. Intell. Med., vol. 29, no. 3, pp. 261–278, 2015. | spa |
| dcterms.references | A. J. Jetter and K. Kok, “Fuzzy Cognitive Maps for futures studies-A methodological assessment of concepts and methods,” Futures, vol. 61, pp. 45–57, 2014. | spa |
| dcterms.references | E. S. Vergini and P. P. Groumpos, “A new conception on the Fuzzy Cognitive Maps method,” IFAC-PapersOnLine, vol. 49, no. 29, pp. 300–304, 2016. | spa |
| dcterms.references | A. Amirkhani, E. I. Papageorgiou, A. Mohseni, and M. R. Mosavi, “A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications,” Computer Methods and Programs in Biomedicine, vol. 142. pp. 129–145, 2017. | spa |
| dcterms.references | A. Al Farsi, F. Doctor, D. Petrovic, S. Chandran, and C. Karyotis, “Interval Valued Data Enhanced Fuzzy Cognitive Maps : Torwards an Appraoch for Autism Deduction in Toddlers,” 2017 | spa |
| dcterms.references | M. S. Mythili and A. R. Mohamed Shanavas, “Meta Heuristic based Fuzzy Cognitive Map Approach to Support towards Early Prediction of Cognitive Disorders among Children (MEHECOM),” Indian J. Sci. Technol., vol. 9, no. 3, 2016 | spa |
| dcterms.references | T. J. (University of N. M. Ross, Fuzzy logic with engineering applications. 2010. | spa |
| dcterms.references | E. I. Papageorgiou and J. L. Salmeron, “A review of fuzzy cognitive maps research during the last decade,” IEEE Trans. Fuzzy Syst., vol. 21, no. 1, pp. 66–79, 2013. | spa |
| dcterms.references | P. P, Groumpos, “Fuzzy Cognitive Maps Basic Theories and Their Application to Complex Systems,” in Fuzzy Cognitive Maps Advances in Theory, Methodologies, Tools And Applications, 2010, pp. 17–39. | spa |
| dcterms.references | E. I. Papageorgiou and J. L. Salmeron, “Learning fuzzy grey cognitive maps using nonlinear Hebbian-based approach,” Int. J. Approx. Reason., vol. 53, no. 1, pp. 54–65, 2012. | spa |
| dcterms.references | V. Subbaraju, S. Sundaram, S. Narasimhan, and M. B. Suresh, “Accurate detection of autism spectrum disorder from structural MRI using extended metacognitive radial basis function network,” Expert Syst. Appl., vol. 42, no. 22, pp. 8775–8790, 2015. | spa |
| dcterms.references | A. Rosenberg, J. S. Patterson, and D. E. Angelaki, “A computational perspective on autism,” Proc. Natl. Acad. Sci., vol. 112, no. 30, pp. 9158–9165, 2015. | spa |
| dcterms.references | E. Puerto, “Avances en el conocimiento y modelado computacional del cerebro autista : Una revisión de literatura" Cuaderno Activa, No. 8, pp. 109–125, 2017. | spa |
| dcterms.references | A. Crippa et al., “Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities,” J. Autism Dev. Disord., vol. 45, no. 7, pp. 2146–2156, 2015. | spa |
| dcterms.references | D. Bone, M. S. Goodwin, M. P. Black, C. C. Lee, K. Audhkhasi, and S. Narayanan, “Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises,” J. Autism Dev. Disord., vol. 45, no. 5, pp. 1121–1136, 2015. | spa |
| dcterms.references | J. Aguilar, “Multilayer Cognitive Maps in the Resolution of Problems using the FCM Designer Tool,” Appl. Artif. Intell., vol. 30, no. 7, pp. 720–743, 2016. | spa |
| dcterms.references | J. Aguilar, J. Hidalgo, F. Osuna, and N. Pérez, “Multilayer cognitive maps to model problems,” in 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, 2016, pp. 1547–1553. | spa |
| dcterms.references | A. Jose and J. Contreras, “The FCM designer tool,” in Studies in Fuzziness and Soft Computing, 2010, vol. 247, pp. 71–87 | spa |
| dcterms.references | J. Aguilar, “Different dynamic causal relationship approaches for cognitive maps,” Appl. Soft Comput. J., vol. 13, no. 1, pp. 271–282, 2013. | spa |
| dcterms.references | E. I. Papageorgiou, C. D. Stylios, and P. P. Groumpos, “An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps,” IEEE Trans Biomed Eng, vol. 50, no. 12, pp. 1326–1339, 2003 | spa |
| dcterms.references | “Diagnostic Instruments in Autistic Spectrum Disorders - Handbook of Autism and Pervasive Development.” | spa |
| dcterms.references | C. Lord et al., “Autism Diagnostic Observation Schedule (ADOS),” Journal of Autism and Developmental Disorders, vol. 30, no. 3. pp. 205–23, 2000. | spa |
| dcterms.references | C. Lord, M. Rutter, P. DiLavore, S. Risi, and K. Gotham, “Autism diagnostic observation schedule, (ADOS-2) modules 1-4,” Los Angeles, Calif., 2012. | spa |
| dcterms.references | C. Lord and C. Corsello, “Diagnostic Instruments in Autistic Spectrum Disorders,” Handb. autism pervasive Dev. Disord., vol. 2: Assessm, pp. 730–771, 2005 | spa |
| dcterms.references | C. Lord et al., “The Autism Diagnostic Observation Schedule - Generic: A standard mesure of social and communication deficits associated with the spectrum of autism,” J. Autism Dev. Disord., vol. 30, no. 3, pp. 205–223, 2000. | spa |
| dcterms.references | J. McClintock, M and J. Fraser, “Diagnostic instruments for autism spectrum disorder,” no. April, p. 30, 2011. | spa |
| dcterms.references | M. Rutter, A. LeCouteur, and C. Lord, “Autism Diagnostic Interview - Revised (ADI-R),” Statew. Agric. L. Use Baseline 2015, vol. 1, 2015. | spa |
| dcterms.references | A. Stabel et al., “Diagnostic Instruments in Autistic Spectrum Disorders,” in Encyclopedia of Autism Spectrum Disorders, vol. 2: Assessm, 2013, pp. 919–926. | spa |
| dcterms.references | P. A. Filipek et al., “Practice parameter: Screening and diagnosis of autism Report of the Quality Standards Subcommittee of the American Academy of Neurology and the Child,” Neurology, vol. 55, no. August, pp. 468–479, 2000. | spa |
| dcterms.references | K. Papanikolaou “Using the Autism Diagnostic Interview-Revised and the Autism Diagnostic Obser.” J Autism Dev Disord. vol. 39, pp. 414-420, 2009. | spa |
| dcterms.references | K. M. Gray, B. J. Tonge, and D. J. Sweeney, “Using the autism diagnostic interviewrevised and the autism diagnostic observation schedule with young children with developmental delay: Evaluating diagnostic validity,” J. Autism Dev. Disord., vol. 38, no. 4, pp. 657–667, 2008. | spa |
| dcterms.references | S. H. Kim and C. Lord, “Combining information from multiple sources for the diagnosis of autism spectrum disorders for toddlers and young preschoolers from 12 to 47 months of age,” J. Child Psychol. Psychiatry Allied Discip., vol. 53, no. 2, pp. 143–151, 2012. | spa |
| dcterms.references | K. Papanikolaou et al., “Using the autism diagnostic Interview-Revised and the Autism diagnostic Observation Schedule-Generic for the diagnosis of Autism spectrum disorders in a Greek sample with a wide range of intellectual abilities,” J. Autism Dev. Disord., vol. 39, no. 3, pp. 414–420, 2009. | spa |
| dcterms.references | E. Zander, H. Sturm, and S. Bölte, “The added value of the combined use of the Autism Diagnostic Interview–Revised and the Autism Diagnostic Observation Schedule: Diagnostic validity in a clinical Swedish sample of toddlers and young preschoolers,” Autism, vol. 19, no. 2, pp. 187–199, 2015. | spa |
| dcterms.references | L. Parisi, T. Di Filippo, and M. Roccella, “The child with autism spectrum disorders (asds) : behavioral and neurobiological aspects,” Acta Medica Mediterr., vol. 21, pp. 1187– 1194, 2015. | spa |
| dcterms.references | T. Charman and K. Gotham, “Measurement Issues: Screening and diagnostic instruments for autism spectrum disorders - lessons from research and practise,” Child Adolesc. Ment. Health, vol. 18, no. 1, pp. 52–63, 2013. | spa |
| dcterms.references | J. Aguilar, “A Survey about Fuzzy Cognitive Maps Papers (Invited Paper),” Int. J. Comput. Cogn., vol. 3, no. 2, pp. 27–33, 2005. | spa |
| dcterms.references | B. Kosko, “Fuzzy cognitive maps,” Int. J. Man. Mach. Stud., vol. 24, no. 1, pp. 65–75, 1986. | spa |
| dcterms.references | B. Galitsky, “A computational simulation tool for training autistic reasoning about mental attitudes,” Knowledge-Based Syst., vol. 50, pp. 25–43, 2013. | spa |
| dcterms.references | M. Reyes, P. Ponce, D. Grammatikou, and A. Molina, “Methodology to weight evaluation areas from autism spectrum disorder ADOS-G test with artificial neural networks and taguchi method,” Rev. Mex. Ing. Biomed., vol. 35, no. 3, pp. 223–240, 2014. | spa |
| dcterms.references | A. Kannappan, A. Tamilarasi, and E. I. Papageorgiou, “Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder,” Expert Syst. Appl., vol. 38, no. 3, pp. 1282–1292, 2011. | spa |
| dcterms.references | J. Ojeda, “A method based on genetic algorithms to support TEA diagnosis Un método basado en algoritmos genéticos de apoyo al diagnóstico TEA,” Actas Ing., vol. 1, pp. 84– 93, 2015. | spa |
| dcterms.references | D. Bone, S. L. Bishop, M. P. Black, M. S. Goodwin, C. Lord, and S. S. Narayanan, “Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion,” J. Child Psychol. Psychiatry Allied Discip., vol. 57, no. 8, pp. 927–937, 2016. | spa |
| dcterms.references | E. I. Papageorgiou and A. Kannappan, “Fuzzy cognitive map ensemble learning paradigm to solve classification problems: Application to autism identification,” Appl. Soft Comput., vol. 12, no. 12, pp. 3798–3809, 2012. | spa |
| dcterms.references | V. Subbaraju, et al. "Accurate detection of autism spectrum disorder from structural MRI using extended metacognitive radial basis function network." Expert Systems with Applications 42.22, 2015 | spa |
| dcterms.references | F. Zhang, et al. "Whole brain white matter connectivity analysis using machine learning: an application to autism." NeuroImage, 2017. | spa |
| dcterms.references | R. Anirudh, and J. Jayaraman. "Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification." arXiv preprint arXiv:1704.07487 2017 | spa |
| dcterms.references | I. L. Cohen, V. Sudhalter, D. Landon-Jimenez, and M. Keogh, “A neural network approach to the classification of autism.,” J. Autism Dev. Disord., vol. 23, no. 3, pp. 443– 66, 1993. | spa |
| dcterms.references | K. Arthi and A. Tamilarasi, “Prediction of autistic disorder using neuro fuzzy system by applying ANN technique,” Int. J. Dev. Neurosci., vol. 26, no. 7, pp. 699–704, 2008 | spa |
| dcterms.references | D. P. Wall, R. Dally, R. Luyster, J. Y. Jung, and T. F. DeLuca, “Use of artificial intelligence to shorten the behavioral diagnosis of autism,” PLoS One, vol. 7, no. 8, 2012. | spa |
| dcterms.references | D. P. Wall, J. Kosmicki, T. F. DeLuca, E. Harstad, and V. A. Fusaro, “Use of machine learning to shorten observation-based screening and diagnosis of autism,” Transl. Psychiatry, vol. 2, no. 4, p. e100, 2012 | spa |
| dcterms.references | L. Tarantino, M. Mazza, M. Valenti, and G. De Gasperis, “Towards an Integrated Approach to Diagnosis, Assessment and Treatment in Autism Spectrum Disorders via a Gamified TEL System,” in methodologies and intelligent systems for technology enhanced learning (mis4tel), 2016, vol. 478, pp. 141–149. | spa |
| dcterms.references | B. Kosko, Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications, vol. 24, no. 1. 2010. | spa |
| dcterms.references | E. I. Papageorgiou and C. D. Stylios, “Fuzzy Cognitive Maps,” in Handbook of Granular Computing, 2008, pp. 755–774. | spa |
| dcterms.references | E. I. Papageorgiou and D. K. Iakovidis, “Intuitionistic fuzzy cognitive maps,” IEEE Trans. Fuzzy Syst., vol. 21, no. 2, pp. 342–354, 2013. | spa |
| dcterms.references | A. Christoforou and A. S. Andreou, “A framework for static and dynamic analysis of multi-layer fuzzy cognitive maps,” Neurocomputing, vol. 232, pp. 133–145, 2017. | spa |
| dcterms.references | N. Akshoomoff et al., “Outcome Classification of Preschool Children With Autism Spectrum Disorders Using MRI Brain Measures,” J. Am. Acad. Child Adolesc. Psychiatry, vol. 43, no. 3, pp. 349–357, 2004. | spa |
| dcterms.references | A. Application, E. Purpose, C. M. Health, P. Inpatient, H. Plan, and H. Services, “State of Michigan Department of Health and Human Services Autism Application : ADOS-2 Evaluation State of Michigan Department of Health and Human Services Autism Application : ADOS-2 Evaluation,” pp. 7–10 | spa |
| dcterms.references | C. P. Johnson and S. M. Myers, “Identification and evaluation of children with autism spectrum disorders.,” Pediatrics, vol. 120, no. 5, pp. 1183–1215, 2007. | spa |
| dcterms.references | A. Application, E. Purpose, C. M. Health, P. Inpatient, H. Plan, and H. Services, “State of Michigan Department of Health and Human Services Autism Application : ADOS-2 Evaluation State of Michigan Department of Health and Human Services Autism Application : ADOS-2 Evaluation,” pp. 7–10. | spa |
| dcterms.references | H. Abbas, F. Garberson, E. Glover and D. P. Wall, "Machine learning for early detection of autism (and other conditions) using a parental questionnaire and home video screening," 2017 IEEE International Conference on Big Data (Big Data), 2017, pp. 3558-3561. | spa |
| dcterms.references | B. van den Bekerom, "Using Machine Learning for Detection of Autism Spectrum Disorder", Technical Report, University of Twente, The Netherlands, 2018 | spa |
| dc.identifier.doi | https://doi.org/10.1016/j.asoc.2018.10.034 | |
| dc.publisher.place | Países Bajos | spa |
| dc.relation.citationedition | Vol.75 (2019) | spa |
| dc.relation.citationendpage | 71 | spa |
| dc.relation.citationissue | (2019) | spa |
| dc.relation.citationstartpage | 58 | spa |
| dc.relation.citationvolume | 75 | spa |
| dc.relation.cites | Puerto, E., Aguilar, J., López, C., & Chávez, D. (2019). Using multilayer fuzzy cognitive maps to diagnose autism spectrum disorder. Applied soft computing, 75, 58-71. | |
| dc.relation.ispartofjournal | Applied Soft Computing | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.creativecommons | Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) | spa |
| dc.subject.proposal | Autism Spectrum Disorder | eng |
| dc.subject.proposal | Multilayer Fuzzy Cognitive Map | eng |
| dc.subject.proposal | Medical Decision Support Systems | eng |
| dc.subject.proposal | Autism Diagnostic Observation Schedule | eng |
| dc.subject.proposal | Autism Diagnostic Interview Revised | eng |
| dc.type.coar | http://purl.org/coar/resource_type/c_6501 | spa |
| dc.type.content | Text | spa |
| dc.type.driver | info:eu-repo/semantics/article | spa |
| dc.type.redcol | http://purl.org/redcol/resource_type/ART | spa |
| oaire.accessrights | http://purl.org/coar/access_right/c_16ec | spa |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | spa |
| dc.type.version | info:eu-repo/semantics/publishedVersion | spa |
