Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder which has an increasing prevalence in children. ASD is clinically highly heterogeneous and lacks objective diagnostic criteria. In recent years, magnetic resonance imaging and genomics have been widely used in the diagnosis of ASD, and some valuable biomarkers have been found, which has improved people’s understanding of the neural and molecular development mechanism of ASD. However, most studies focus on limited data sources with lack of integration research, thus leading to inconsistent or biased results. In this paper, we design a compute-aided diagnosis framework based on multiple ASD-relevant data sources for purpose of distinguishing the ASD patients more accurately. We first establish a multiple data collection procedure from initial diagnosis to regular follow-up visits. Various medical big data including structured and unstructured forms are collected from different devices or protocols and then they are deposited and accessed based on Hadoop platform. Furthermore, we design a classification framework to identify ASD patients by integrating the complementary information from multiple data sources. Deep learning is used to extract features from each data source automatically, and then all extracted features are integrated by the multiple kernel learning method for improving the diagnostic accuracy of ASD.
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Simonoff, E., et al.: Psychiatric disorders in children with autism spectrum disorders: prevalence, comorbidity, and associated factors in a population-derived sample. J. Am. Acad. Child Adolesc. Psychiatry 47(8), 921–929 (2008)
Hu, R.J.: Diagnostic and Statistical Manual of Mental Disorders (DSM-IV). Academic Press, New York (2003)
Gotham, K., Pickles, A., Lord, C.: Standardizing ADOS scores for a measure of severity in autism spectrum disorders. J. Autism Dev. Disord. 39(5), 693–705 (2009)
Cox, A., et al.: Autism spectrum disorders at 20 and 42 months of age: stability of clinical and ADI-R diagnosis. J. Child Psychol. Psychiatry 40(5), 719–732 (1999)
Just, M.A., Cherkassky, V.L., Keller, T.A., Minshew, N.J.: Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity. Brain J. Neurol. 127(Pt 8), 1811–1821 (2004)
McFadden, K., Minshew, N.J.: Evidence for dysregulation of axonal growth and guidance in the etiology of ASD. Front. Hum. Neurosci. 7, 671 (2013)
Qureshi, A.Y., et al.: Opposing brain differences in 16p11.2 deletion and duplication carriers. J. Neurosci. Off. J. Soc. Neurosci. 34(34), 11199–11211 (2014)
Dennis, E.L., et al.: Altered structural brain connectivity in healthy carriers of the autism risk gene, CNTNAP2. Brain Connect. 1(6), 447–459 (2011)
Rudie, J.D., et al.: Autism-associated promoter variant in MET impacts functional and structural brain networks. Neuron 75(5), 904–915 (2012)
Insel, T., et al.: Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167(7), 748–751 (2010)
Payakachat, N., Tilford, J.M., Ungar, W.J.: National Database for Autism Research (NDAR): big data opportunities for health services research and health technology assessment. Pharm. Econ. 34(2), 127–138 (2016)
Buxbaum, J.D., et al.: The autism simplex collection: an international, expertly phenotyped autism sample for genetic and phenotypic analyses. Molecular autism 534 (2014)
Geschwind, D.H., et al.: The autism genetic resource exchange: a resource for the study of autism and related neuropsychiatric conditions. Am. J. Hum. Genet. 69(2), 463–466 (2001)
Di Martino, A., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19(6), 659–667 (2014)
Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R., Muharemagic, E.: Deep learning applications and challenges in big data analytics. J. Big Data 2(1), 1 (2015)
Yu, H., Samuels, D.C., Zhao, Y.Y., Guo, Y.: Architectures and accuracy of artificial neural network for disease classification from omics data. BMC Genom. 20(1), 167 (2019)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Rahimi, A., Gonen, M.: Discriminating early- and late-stage cancers using multiple kernel learning on gene sets. Bioinformatics 34(13), i412–i421 (2018)
Tao, M., et al.: Classifying breast cancer subtypes using multiple kernel learning based on omics data. Genes 10(3) (2019)
Rakotomamonjy, A., Bach, F., Canu, S., Grandvalet, Y.: SimpleMKL. J. Mach. Learn. Res. 9(11) (2008)
Bach, F.: Consistency of the group Lasso and multiple kernel learning. Comput. Sci. (2007)
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This research was supported by a grant from National Natural Science Foundation of China (No: 61772375) and Independent Research Project of School of Information Management Wuhan University (No: 413100032).
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Chen, T. (2019). A Comprehensive Database Based on Multiple Data Sources to Facilitate Diagnosis of ASD. In: Chen, H., Zeng, D., Yan, X., Xing, C. (eds) Smart Health. ICSH 2019. Lecture Notes in Computer Science(), vol 11924. Springer, Cham. https://doi.org/10.1007/978-3-030-34482-5_10
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DOI: https://doi.org/10.1007/978-3-030-34482-5_10
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