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A Model to Support the Prediction of Indicators in the Diagnosis and Intervention of Autism Spectrum Disorder

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Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023) (UCAmI 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 842))

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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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Authors and Affiliations

  1. Department of Computer Technology and Computation, University of Alicante, 03690, Alicante, Spain

    Vanessa Ramos, David Gil & Higinio Mora

  2. Department of Psychology, University of Castilla la Mancha, Ciudad Real, Spain

    Tania Mondéjar

  3. GPLSI Research Group, Department of Software and Computing Systems, University of Alicante, 03690, Alicante, Spain

    Antonio Ferrández

  4. LUCENTIA Research Group, Department of Software and Computing Systems, University of Alicante, 03690, Alicante, Spain

    Jesús Peral

Authors
  1. Vanessa Ramos
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  2. Tania Mondéjar
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  3. Antonio Ferrández
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  4. Jesús Peral
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  5. David Gil
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  6. Higinio Mora
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Corresponding author

Correspondence to David Gil .

Editor information

Editors and Affiliations

  1. Ciudad Real, Spain

    José Bravo

  2. Mérida, Yucatán, Mexico

    Gabriel Urzáiz

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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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