Artificial intelligence-based approaches for improving the diagnosis, triage, and prioritization of autism spectrum disorder: a systematic review of current trends and open issues-Reference-Cited by-同舟云学术

Artificial intelligence-based approaches for improving the diagnosis, triage, and prioritization of autism spectrum disorder: a systematic review of current trends and open issues

Published:2023-06-21 Issue:S1 Volume:56 Page:53-117
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Joudar Shahad Sabbar,Albahri A. S.^ORCID,Hamid Rula A.,Zahid Idrees A.,Alqaysi M. E.,Albahri O. S.,Alamoodi A. H.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

https://link.springer.com/content/pdf/10.1007/s10462-023-10536-x.pdf

Reference128 articles.

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2. Abdelhamid N, Padmavathy A, Peebles D, Thabtah F, Goulder-Horobin D (2020) Data imbalance in autism pre-diagnosis classification systems: an experimental study. J Inf Knowl Manag 19(1):1–16. https://doi.org/10.1142/S0219649220400146

3. Abdolzadegan D, Moattar MH, Ghoshuni M (2020) A robust method for early diagnosis of autism spectrum disorder from EEG signals based on feature selection and DBSCAN method. Biocybern Biomed Eng 40(1):482–493. https://doi.org/10.1016/j.bbe.2020.01.008

4. Abrahams BS et al (2013) SFARI gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs). Mol Autism 4(1):1–3. https://doi.org/10.1186/2040-2392-4-36

5. Aghdam MA, Sharifi A, Pedram MM (2019) Diagnosis of autism spectrum disorders in young children based on resting-state functional magnetic resonance imaging data using convolutional neural networks. J Digit Imaging 32(6):899–918. https://doi.org/10.1007/S10278-019-00196-1

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