지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2019.10
- 수록면
- 145 - 152 (8page)
이용수
초록· 키워드
오류제보하기
Objectives: The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective data-driven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics.
Methods: Based on our search and exclusion criteria, we reviewed 13 studies.
Results: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data.
Conclusion: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.
Methods: Based on our search and exclusion criteria, we reviewed 13 studies.
Results: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data.
Conclusion: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.
목차
INTRODUCTION
METHODS
RESULTS
DISCUSSION
CONCLUSION
REFERENCES
참고문헌 (49)
참고문헌 신청
American Psychiatric Association / 2013 / Diagnostic and Statistical Manual of Mental Disorder-5 :50~59
Goldani AA / 2014 / Biomarkers in autism / Front Psychiatry 5:100
Falkmer T / 2013 / Diagnostic procedures in autism spectrum disorders: a systematic literature review / Eur Child Adolesc Psychiatry 22:329~340
Taylor LJ / 2017 / Brief report: an exploratory study of the diagnostic reliability for autism spectrum disorder / J Autism Dev Disord 47:1551~1558
Randall M / 2016 / Diagnosing autism: Australian paediatric research network surveys / J Paediatr Child Health 52:11~17
Goldani AA / 2014 / Biomarkers in autism / Front Psychiatry 5:100
Falkmer T / 2013 / Diagnostic procedures in autism spectrum disorders: a systematic literature review / Eur Child Adolesc Psychiatry 22:329~340
Taylor LJ / 2017 / Brief report: an exploratory study of the diagnostic reliability for autism spectrum disorder / J Autism Dev Disord 47:1551~1558
Randall M / 2016 / Diagnosing autism: Australian paediatric research network surveys / J Paediatr Child Health 52:11~17
함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Autism Spectrum Disorder and Behavioral Intervention : An Updated Review
소아청소년정신의학
2015 .06
-
자폐스펙트럼장애의 조기선별에 대한 관련 분야 종사자의 인식 조사
소아청소년정신의학
2017 .04
-
Mental Health Literacy of Autism Spectrum Disorder in Korea: Recognition, Etiology, Treatment
PSYCHIATRY INVESTIGATION
2019 .01
-
Recent update of autism spectrum disorders
Korean journal of pediatrics
2015 .01
-
Loss of Acquired Skills: Regression in Young Children With Autism Spectrum Disorders
소아청소년정신의학
2023 .01
-
Predicting Autism Spectrum Disorder Using Blood-based Gene Expression Signatures and Machine Learning
Clinical Psychopharmacology and Neuroscience
2017 .01
-
The Wnt Signaling Pathway and Related Therapeutic Drugs in Autism Spectrum Disorder
Clinical Psychopharmacology and Neuroscience
2018 .01
-
New System & New Therapy for ASD in Korea
대한소아청소년정신의학회 학술대회논문집
2023 .11
-
자폐 스펙트럼 장애 섭식장애 평가의 이해: 문헌 고찰
재활치료과학
2024 .05
-
How Are Autism Spectrum Disorder and Savant Skills Treated in Cinematographic Works? A Study Focusing on Korean Movies and TV Series
소아청소년정신의학
2023 .04
-
자폐스펙트럼장애 아동들의 효과적인 조기 중재에 대한 체계적 고찰
재활치료과학
2018 .01
-
Visual Perception in Autism Spectrum Disorder: A Review of Neuroimaging Studies
소아청소년정신의학
2020 .07
-
자폐 스펙트럼 장애 선별 도구에 대한 고찰
신경정신의학
2019 .01
-
자폐스펙트럼장애(Autism Spectrum Disorder) 소아청소년에서의 과체중 및 비만 연구 - 후향적 의무기록 조사 -
정신신체의학
2015 .01
-
A Randomized, Placebo-controlled Trial of Digestive Enzymes in Children with Autism Spectrum Disorders
Clinical Psychopharmacology and Neuroscience
2015 .01
-
Prevalence and Premature Mortality Statistics of Autism Spectrum Disorder Among Children in Korea: A Nationwide Population-Based Birth Cohort Study
Journal of Korean Medical Science
2022 .01
-
An update on the cause and treatment of sleep disturbance in children and adolescents with autism spectrum disorder
Journal of Yeungnam Medical Science
2021 .10
-
자폐 스펙트럼 장애 환아 1례에 대한 증례 보고
대한한방소아과학회지
2017 .01
-
자폐 스펙트럼 장애 인식도 및 한의치료 인식도에 관한 단면연구 - 환아의 부모를 대상으로
대한한방소아과학회지
2021 .01
-
Autism and Beauty : Neural Correlates of Aesthetic Experiences in Autism Spectrum Disorder
소아청소년정신의학
2018 .07
이 논문의 저자 정보
이 논문과 함께 이용한 논문
최근 본 자료
댓글(0)
0