Abstract
Autism Spectrum Disorder (ASD) is a developmental disability primarily characterized by challenges in social interaction and communication. Due to the unknown etiology of ASD, numerous computational psychiatry research studies have been carried out to identify pertinent features and uncover hidden correlations to detect this type of disability at an early stage. The aim of this ongoing project is to present the initial tests carried out on autistic children by analysing their conversations or writings to assess their social skills in order to find indicators for the most personalised intervention possible. This model would consist of the most advanced machine learning algorithms and Natural Language Processing techniques (e.g. Transformers or ChatGPT). The paper concludes by presenting a case study that utilized autism data to verify the efficacy of our proposed model, demonstrating remarkably promising findings.
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Acknowledgements
This research has been partially funded by the BALLADEER project (PROMETEO/2021/088) and the project NL4DISMIS (CIPROM/2021/21) from the Consellería Valenciana (Generalitat Valenciana). Furthermore, it has been partially funded by the AETHER-UA (PID2020-112540RB-C43) project from the MCIN and the R &D projects “CORTEX” (PID2021-123956OB-I00), funded by MCIN/ AEI/10.13039/501100011033/. This result has been supported through the Spanish Government by the FEDER project PID2021-127275OB-I00.
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Ramos, V., Mondéjar, T., Ferrández, A., Peral, J., Gil, D., Mora, H. (2023). A Model to Support the Prediction of Indicators in the Diagnosis and Intervention of Autism Spectrum Disorder. In: Bravo, J., Urzáiz, G. (eds) Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023). UCAmI 2023. Lecture Notes in Networks and Systems, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-031-48642-5_7
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