All Outputs (4)

Vision-based approach for autism diagnosis using transfer learning and eye-tracking (2022)
Presentation / Conference Contribution

The potentials of Transfer Learning (TL) have been well-researched in areas such as Computer Vision and Natural Language Processing. This study aims to explore a novel application of TL to detect Autism Spectrum Disorder. We seek to develop an approa... Read More about Vision-based approach for autism diagnosis using transfer learning and eye-tracking.

Learning embeddings from free-text triage notes using pretrained transformer models (2022)
Presentation / Conference Contribution

The advent of transformer models has allowed for tremendous progress in the Natural Language Processing (NLP) domain. Pretrained transformers could successfully deliver the state-of-the-art performance in a myriad of NLP tasks. This study presents an... Read More about Learning embeddings from free-text triage notes using pretrained transformer models.

Eye-tracking dataset to support the research on autism spectrum disorder (2022)
Presentation / Conference Contribution

The availability of data is a key enabler for researchers across different disciplines. However, domains, such as healthcare, are still fundamentally challenged by the paucity and imbalance of datasets. Health data could be inaccessible due to a vari... Read More about Eye-tracking dataset to support the research on autism spectrum disorder.

Use of artificial intelligence to manage patient flow in emergency department during the COVID-19 pandemic: A prospective, single-center study (2022)
Journal Article

BACKGROUND: During the coronavirus disease 2019 (COVID-19) pandemic, calculation of the number of emergency department (ED) beds required for patients with vs. without suspected COVID-19 represented a real public health problem. In France, Amiens Pic... Read More about Use of artificial intelligence to manage patient flow in emergency department during the COVID-19 pandemic: A prospective, single-center study.