Applying Artificial Intelligence for Diagnostic Classification of Korean Autism Spectrum Disorder
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초록
Objective The primary objective of this study was to predict subgroups of autism spectrum disorder (ASD) based on the Diagnostic Statistical Manual for Mental Disorders-IV Text Revision (DSM-IV-TR) by machine learning (ML). The secondary objective was to set up a ranking of Autism Diagnostic Interview-Revised (ADI-R) diagnostic algorithm items based on ML, and to confirm whether ML can sufficiently predict the diagnosis with these minimum items.Methods In the first experiment, a multiclass decision forest algorithm was applied, and the diagnostic algorithm score value of 1,269 Korean ADI-R test data was used for prediction. In the second experiment, we used 539 Korean ADI-R case data (over 48 months with verbal language) to apply mutual information to rank items used in the ADI diagnostic algorithm.Results In the first experiment, the results of predicting in the case of pervasive developmental disorder not otherwise specified as “ASD” were almost three times higher than predicting it as “No diagnosis.” In the second experiment, the top 10 ranking items of ADI-R were mainly related to the quality abnormality of communication.Conclusion In conclusion, we verified the applicability of ML in diagnosis and found that the application of artificial intelligence for rapid diagnosis or screening of ASD patients may be useful.
Abstract
Objective The primary objective of this study was to predict subgroups of autism spectrum disorder (ASD) based on the Diagnostic Statistical Manual for Mental Disorders-IV Text Revision (DSM-IV-TR) by machine learning (ML). The secondary objective was to set up a ranking of Autism Diagnostic Interview-Revised (ADI-R) diagnostic algorithm items based on ML, and to confirm whether ML can sufficiently predict the diagnosis with these minimum items.Methods In the first experiment, a multiclass decision forest algorithm was applied, and the diagnostic algorithm score value of 1,269 Korean ADI-R test data was used for prediction. In the second experiment, we used 539 Korean ADI-R case data (over 48 months with verbal language) to apply mutual information to rank items used in the ADI diagnostic algorithm.Results In the first experiment, the results of predicting in the case of pervasive developmental disorder not otherwise specified as “ASD” were almost three times higher than predicting it as “No diagnosis.” In the second experiment, the top 10 ranking items of ADI-R were mainly related to the quality abnormality of communication.Conclusion In conclusion, we verified the applicability of ML in diagnosis and found that the application of artificial intelligence for rapid diagnosis or screening of ASD patients may be useful.
참고문헌 (25)
- Deo RC, "Machine learning in medicine", Circulation, 132, pp.1920~1930 (2015)
- Doyle-Lindrud S, "Watson will see you now : a supercomputer to help clinicians make informed treatment decisions", Clin J Oncol Nurs, 19, pp.31~32 (2015)
- American Psychiatric Association, "Diagnostic and Statistical Manual of Mental Disorders" (2013), American Psychiatric Association publishing
- Lord C, "The autism diagnostic observation schedule-generic : a standard measure of social and communication deficits associated with the spectrum of autism", J Autism Dev Disord, 30, pp.205~223 (2000)
- Lord C, "A multisite study of the clinical diagnosis of different autism spectrum disorders", Arch Gen Psychiatry, 69, pp.306~313 (2012)
- Lord C, "Autism diagnostic interview-revised : a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders", J Autism Dev Disord, 24, pp.659~685 (1994)
- 송다예, "The Use of Artificial Intelligence in Screening and Diagnosis of Autism Spectrum Disorder: A Literature Review", Journal of the Korean Academy of Child and Adolescent Psychiatry, 30(4), pp.145~152 (2019), 대한소아청소년 정신의학회[DOI]
- Wall DP, "Use of machine learning to shorten observation-based screening and diagnosis of autism", Transl Psychiatry, 2, pp.e100~ (2012)
- Wall DP, "Use of artificial intelligence to shorten the behavioral diagnosis of autism", PLoS One, 7, pp.e43855~ (2012)
- Opitz D, "Popular ensemble methods : an empirical study", J Artif Intell Res, 11, pp.169~198 (1999)
- Oshiro TM, "Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)", pp.154~168 (2012), Springer
- Rokach L, "Decision forest : twenty years of research", Inf Fusion, 27, pp.111~125 (2016)
- Hastie TJ, "Statistical Models in S" (2017), Routledge
- Oza NC, "Classifier ensembles: select real-world applications", Inf Fusion, 9, pp.4~20 (2008)
- Marsland S, "Machine Learning?: An Algorithmic Perspective" (2014), CRC Press
- Breiman L, "Bagging predictors", Mach Learn, 24, pp.123~140 (1996)
- "Experiments with a New Boosting Algorithm" (1996)[URL]
- Freund Y, "A decision-theoretic generalization of on-line learning and an application to boosting", J Comput Syst Sci, 55, pp.119~139 (1997)
- Li J, "Feature selection : a data perspective", ACM Compuing Surv, 50, pp.94~ (2017)
- Quinlan JR, "Induction of decision trees", Mach Learn, 1, pp.81~106 (1986)
- Dietterich TG, "Experimental comparison of three methods for constructing ensembles of decision trees : bagging, boosting, and randomization", Mach Learn, 40, pp.139~157 (2000)
- Criminisi A, "Decision Forests for Computer Vision and Medical Image Analysis" (2013), Springer-Verlag
- Duda M, "Testing the accuracy of an observation-based classifier for rapid detection of autism risk", Transl Psychiatry, 4, pp.e424~ (2011)
- 진서훈, "한국인 자폐장애진단에서의 판별모형", Journal of The Korean Data Analysis Society, 15(2), pp.669~676 (2013), 한국자료분석학회
- Kelarev AV, "Empirical investigation of decision tree ensembles for monitoring cardiac complications of diabetes", Int J Data Warehouse Min, 9, pp.1~18 (2013)