KMID : 1118520200170111090
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Psychiatry Investigation 2020 Volume.17 No. 11 p.1090 ~ p.1095
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Applying Artificial Intelligence for Diagnostic Classification of Korean Autism Spectrum Disorder
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Choi Eun-Soo
Yoo Hee-Jeong Kang Min-Soo Kim Soon-Ae
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Abstract
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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.
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KEYWORD
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Autism spectrum disorder, Autism diagnostic interview-revised, Developmental disorder, Machine learning, Multiclass decision forest
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