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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.1
- 수록면
- 1,090 - 1,095 (6page)
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초록· 키워드
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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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참고문헌 (25)
참고문헌 신청
Deo RC / 2015 / Machine learning in medicine / Circulation 132:1920~1930
Doyle-Lindrud S / 2015 / Watson will see you now : a supercomputer to help clinicians make informed treatment decisions / Clin J Oncol Nurs 19:31~32
American Psychiatric Association / 2013 / Diagnostic and Statistical Manual of Mental Disorders / American Psychiatric Association publishing
Lord C / 2000 / The autism diagnostic observation schedule-generic : a standard measure of social and communication deficits associated with the spectrum of autism / J Autism Dev Disord 30:205~223
Lord C / 2012 / A multisite study of the clinical diagnosis of different autism spectrum disorders / Arch Gen Psychiatry 69:306~313
Doyle-Lindrud S / 2015 / Watson will see you now : a supercomputer to help clinicians make informed treatment decisions / Clin J Oncol Nurs 19:31~32
American Psychiatric Association / 2013 / Diagnostic and Statistical Manual of Mental Disorders / American Psychiatric Association publishing
Lord C / 2000 / The autism diagnostic observation schedule-generic : a standard measure of social and communication deficits associated with the spectrum of autism / J Autism Dev Disord 30:205~223
Lord C / 2012 / A multisite study of the clinical diagnosis of different autism spectrum disorders / Arch Gen Psychiatry 69:306~313
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