All Outputs (5)

Predictive models in emergency medicine and their missing data strategies: a systematic review (2023)
Journal Article
Arnaud, E., Elbattah, M., Ammirati, C., Dequen, G., & Ghazali, D. A. (2023). Predictive models in emergency medicine and their missing data strategies: a systematic review. npj Digital Medicine, 6(1), 28. https://doi.org/10.1038/s41746-023-00770-6

In the field of emergency medicine (EM), the use of decision support tools based on artificial intelligence has increased markedly in recent years. In some cases, data are omitted deliberately and thus constitute “data not purposely collected” (DNPC)... Read More about Predictive models in emergency medicine and their missing data strategies: a systematic review.

Use of artificial intelligence to manage patient flow in emergency department during the COVID-19 pandemic: A prospective, single-center study (2022)
Journal Article
Arnaud, E., Elbattah, M., Ammirati, C., Dequen, G., & Ghazali, D. A. (2022). Use of artificial intelligence to manage patient flow in emergency department during the COVID-19 pandemic: A prospective, single-center study. International Journal of Environmental Research and Public Health, 19(15), e9667. https://doi.org/10.3390/ijerph19159667

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.

Computer-aided screening of autism spectrum disorder: Eye-tracking study using data visualization and deep learning (2021)
Journal Article
Cilia, F., Carette, R., Elbattah, M., Dequen, G., Guérin, J. L., Bosche, J., …Le Driant, B. (2021). Computer-aided screening of autism spectrum disorder: Eye-tracking study using data visualization and deep learning. JMIR Human Factors, 8(4), Article e27706. https://doi.org/10.2196/27706

Background: The early diagnosis of autism spectrum disorder (ASD) is highly desirable but remains a challenging task, which requires a set of cognitive tests and hours of clinical examinations. In addition, variations of such symptoms exist, which ca... Read More about Computer-aided screening of autism spectrum disorder: Eye-tracking study using data visualization and deep learning.

Variational autoencoder for image-based augmentation of eye-tracking data (2021)
Journal Article
Elbattah, M., Loughnane, C., Guérin, J. L., Carette, R., Cilia, F., & Dequen, G. (2021). Variational autoencoder for image-based augmentation of eye-tracking data. Journal of Imaging, 7(5), Article 83. https://doi.org/10.3390/jimaging7050083

Over the past decade, deep learning has achieved unprecedented successes in a diversity of application domains, given large-scale datasets. However, particular domains, such as healthcare, inherently suffer from data paucity and imbalance. Moreover,... Read More about Variational autoencoder for image-based augmentation of eye-tracking data.

Mining the Irish hip fracture database: Learning factors contributing to care outcomes (2020)
Journal Article
Elbattah, M., & Molloy, O. (2020). Mining the Irish hip fracture database: Learning factors contributing to care outcomes. International Journal of Data Science, 5(4), 290. https://doi.org/10.1504/ijds.2020.115875

Data analytics has opened the door for improving many aspects pertaining to the delivery of healthcare. This study avails of unsupervised machine learning to extract knowledge from the Irish hip fracture database (IHFD). The dataset under considerati... Read More about Mining the Irish hip fracture database: Learning factors contributing to care outcomes.