Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning

Translational Psychiatry

Volumn 5, Issue 2, 2015, Pages

  Kosmicki, J A ^a,b   Sochat, V ^c   Duda, M ^c   Wall, D P ^c  

^a MASSACHUSETTS GENERAL HOSPITAL   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; ASPERGER SYNDROME; AUTISM; AUTISM DIAGNOSTIC OBSERVATION SCHEDULE; CHILD; CHILD BEHAVIOR; CLINICAL ASSESSMENT; COGNITION; COHORT ANALYSIS; COMPULSION; FEMALE; HUMAN; LEARNING ALGORITHM; LINGUISTICS; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; PERVASIVE DEVELOPMENTAL DISORDER NOT OTHERWISE SPECIFIED; STATISTICAL ANALYSIS; SUPPORT VECTOR MACHINE; ADOLESCENT; ADULT; ALGORITHM; AUTISTIC DISORDER; COMPUTER ASSISTED DIAGNOSIS; INFANT; MIDDLE AGED; PRESCHOOL CHILD; PROCEDURES; PSYCHOLOGY; PSYCHOMETRY; REPRODUCIBILITY; RISK FACTOR; STATISTICS AND NUMERICAL DATA; YOUNG ADULT;

ADOLESCENT; ADULT; ALGORITHMS; AUTISTIC DISORDER; CHILD; CHILD BEHAVIOR; CHILD, PRESCHOOL; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; INFANT; MACHINE LEARNING; MALE; MIDDLE AGED; PSYCHOMETRICS; REPRODUCIBILITY OF RESULTS; RISK FACTORS; YOUNG ADULT;

EID: 84927748501     PISSN: None     EISSN: 21583188     Source Type: Journal    
DOI: 10.1038/tp.2015.7     Document Type: Article

References (28)

1. Centers for Disease Control and Prevention. Prevalence of autism spectrum disorder among children aged 8 years-autism and developmental disabilities monitoring network, 11 sites, United States, 2010. MMWR Surveill Summ 2014; 63: 1-21.
2. Iossifov I, O'Roak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 2014 11./13 515: 216-221.
3. De Rubeis S, He X, Goldberg AP, Poultney CS, Samocha K, Cicek AE et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 2014; 515: 209-215.
4. Lord C, Risi S, Lambrecht L, Cook EH, Leventhal BL, DiLavore PC et al. The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord 2000; 30: 205-223.
5. Shattuck PT, Durkin M, Maenner M, Newschaffer C, Mandell DS, Wiggins L et al. Timing of identification among children with an autism spectrum disorder: findings from a population-based surveillance study. J Am Acad Child Adolesc Psychiatry 2009; 48: 474-483.
6. Wiggins LD, Baio J, Rice C. Examination of the time between first evaluation and first autism spectrum diagnosis in a population-based sample. J Dev Behav Pediatr 2006; 27: 79-87.
7. Bernier R, Mao A, Yen J. Psychopathology, families, and culture: autism. Child Adolesc Psychiatr Clin N Am 2010; 19: 855-867.
8. Howlin P. Children with autism and Asperger syndrome: a guide for practitioners and parents. John Wiley & Sons, Ltd: Chichester, UK, 1998.
9. Pinto-Martin JA, Young LM, Mandell DS, Poghosyan L, Giarelli E, Levy SE. Screening strategies for autism spectrum disorders in pediatric primary care. J Dev Behav Pediatr 2008; 29: 345-350.
10. Wall DP, Kosmicki J, Deluca TF, Harstad E, Fusaro VA. Use of machine learning to shorten observation-based screening and diagnosis of autism. Transl Psychiatry 2012; 2: e100.
11. Duda M, Kosmicki JA, Wall DP. Testing the accuracy of an observation-based classifier for rapid detection of autism risk. Transl Psychiatry 2014; 4: e424.
12. Wall DP, Dally R, Luyster R, Jung J-Y, Deluca TF. Use of artificial intelligence to shorten the behavioral diagnosis of autism. PLoS One. 2012; 7: e43855.
13. Gotham K, Pickles A, Lord C. Trajectories of autism severity in children using standardized ADOS scores. Pediatrics 2012; 130: 1278-1284.
14. Gotham K, Risi S, Pickles A, Lord C. The Autism Diagnostic Observation Schedule: revised algorithms for improved diagnostic validity. J Autism Dev Disord 2007; 37: 613-627.
15. Fischbach GD, Lord C. The Simons Simplex Collection: a resource for identification of autism genetic risk factors. Neuron 2010; 68: 192-195.
16. Geschwind DH, Sowinski J, Lord C, Iversen P, Shestack J, Jones P et al. The autism genetic resource exchange: a resource for the study of autism and related neuropsychiatric conditions. Am J Hum Genet 2001; 69: 463-466.
17. Hall D, Huerta MF, McAuliffe MJ, Farber GK. Sharing heterogeneous data: the national database for autism research. Neuroinformatics 2012; 10: 331-339.
18. The Simons Vip Consortium. Simons Variation in Individuals Project (Simons VIP): a genetics-first approach to studying autism spectrum and related neurodevelopmental disorders. Neuron 2012, 73: 1063-1067.
19. Shinawi M, Liu P, Kang S-HL, Shen J, Belmont JW, Scott DA et al. Recurrent reciprocal 16p11.2 rearrangements associated with global developmental delay, behavioural problems, dysmorphism, epilepsy, and abnormal head size. J Med Genet 2010; 47: 332-341.
20. John GH Kohavi R Pfleger K. (eds). Irrelevant features and the subset selection problem. Machine Learning: Proceedings of the Eleventh International Conference. San Francisco, CA, USA: Morgan Kaufmann Publisher, 1994, 121-129.
21. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. SIGKDD Explor Newsl 2009; 11: 10-18.
22. Le Cessie S, Van Houwelingen J. Ridge estimators in logistic regression. Appl Statist 1992; 41: 191-201.
23. Landwehr N, Hall M, Frank E. Logistic model trees. Mach Learn 2005; 59: 161-205.
24. Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2011; 2: 27.
25. Hallmayer J, Cleveland S, Torres A, Phillips J, Cohen B, Torigoe T et al. Genetic heritability and shared environmental factors among twin pairs with autism. Arch Gen Psychiatry 2011; 68: 1095-1102.
26. Tenev A, Markovska-Simoska S, Kocarev L, Pop-Jordanov J, Muller A, Candrian G. Machine learning approach for classification of ADHD adults. Int J Psychophysiol 2014; 93: 162-166.
27. Fusaro VA, Daniels J, Duda M, DeLuca TF, D'Angelo O, Tamburello J et al. The potential of accelerating early detection of autism through content analysis of YouTube videos. PLoS One 2014; 9: 4.
28. Lord C, Risi S, DiLavore PS, Shulman C, Thurm A, Pickles A. Autism from 2 to 9 years of age. Arch Gen Psychiatry 2006; 63: 694-701.