A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder
| Autor: | Sadiya Tahir, Zurki Ibrahim, Nadire Cavus, Usama Ishaq Abdulrazak, Abdullahi Dahiru, Abdulmalik Ahmad Lawan, Adamu Hussaini |
|---|---|
| Přispěvatelé: | Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804 (CRESTIC), Université de Reims Champagne-Ardenne (URCA) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
diagnosis
Medicine (miscellaneous) lcsh:Medicine autism spectrum disorder Review Diagnostic system Machine learning computer.software_genre Diagnostic tools [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 03 medical and health sciences 0302 clinical medicine medicine 0501 psychology and cognitive sciences business.industry screening 05 social sciences lcsh:R Behavioral assessment Conceptual basis medicine.disease artificial intelligence Systematic review Behavioral data machine learning Autism spectrum disorder Artificial intelligence business Psychology computer 030217 neurology & neurosurgery 050104 developmental & child psychology |
| Zdroj: | Journal of Personalized Medicine Journal of Personalized Medicine, MDPI, 2021, 11 (4), pp.299. ⟨10.3390/jpm11040299⟩ Journal of Personalized Medicine, Vol 11, Iss 299, p 299 (2021) |
| ISSN: | 2075-4426 |
| DOI: | 10.3390/jpm11040299⟩ |
| Popis: | International audience; Autism spectrum disorder (ASD) is associated with significant social, communication, and behavioral challenges. The insufficient number of trained clinicians coupled with limited accessibility to quick and accurate diagnostic tools resulted in overlooking early symptoms of ASD in children around the world. Several studies have utilized behavioral data in developing and evaluating the performance of machine learning (ML) models toward quick and intelligent ASD assessment systems. However, despite the good evaluation metrics achieved by the ML models, there is not enough evidence on the readiness of the models for clinical use. Specifically, none of the existing studies reported the real-life application of the ML-based models. This might be related to numerous challenges associated with the data-centric techniques utilized and their misalignment with the conceptual basis upon which professionals diagnose ASD. The present work systematically reviewed recent articles on the application of ML in the behavioral assessment of ASD, and highlighted common challenges in the studies, and proposed vital considerations for real-life implementation of ML-based ASD screening and diagnostic systems. This review will serve as a guide for researchers, neuropsychiatrists, psychologists, and relevant stakeholders on the advances in ASD screening and diagnosis using ML. |
| Databáze: | OpenAIRE |
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