All Outputs (17)

Explainable NLP model for predicting patient admissions at emergency department using triage notes (2024)
Conference Proceeding
Arnaud, E., Elbattah, M., Moreno-Sánchez, P. A., Dequen, G., & Ghazali, D. A. (2024). Explainable NLP model for predicting patient admissions at emergency department using triage notes. In 2023 IEEE International Conference on Big Data (BigData) (4843-4847). https://doi.org/10.1109/bigdata59044.2023.10386753

Explainable Artificial Intelligence (XAI) has the potential to revolutionize healthcare by providing more transparent, trustworthy, and understandable predictions made by AI models. To this end, the present study aims to develop an explainable NLP mo... Read More about Explainable NLP model for predicting patient admissions at emergency department using triage notes.

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.

Applications of machine learning methods to assist the diagnosis of autism spectrum disorder (2023)
Book Chapter
Elbattah, M., Carette, R., Cilia, F., Guérin, J., & Dequen, G. (2023). Applications of machine learning methods to assist the diagnosis of autism spectrum disorder. In Neural Engineering Techniques for Autism Spectrum Disorder, Volume 2: Diagnosis and Clinical Analysis (99-119). Elsevier. https://doi.org/10.1016/B978-0-12-824421-0.00013-8

Autism spectrum disorder (ASD) is a lifelong neuro-developmental disorder that is generally marked by a set of communication and social impairments. The early diagnosis of autism is genuinely beneficial for the welfare of children and parents as well... Read More about Applications of machine learning methods to assist the diagnosis of autism spectrum disorder.

Learning embeddings from free-text triage notes using pretrained transformer models (2022)
Conference Proceeding
Arnaud, É., Elbattah, M., Gignon, M., & Dequen, G. (2022). Learning embeddings from free-text triage notes using pretrained transformer models. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (835-841). https://doi.org/10.5220/0011012800003123

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)
Conference Proceeding
Cilia, F., Carette, R., Elbattah, M., Guérin, J., & Dequen, G. (2022). Eye-tracking dataset to support the research on autism spectrum disorder. In Proceedings of the 1st Workshop on Scarce Data in Artificial Intelligence for Healthcare (59-64). https://doi.org/10.5220/0011540900003523

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.

Vision-based approach for autism diagnosis using transfer learning and eye-tracking (2022)
Conference Proceeding
Elbattah, M., Guérin, J., Carette, R., Cilia, F., & Dequen, G. (2022). Vision-based approach for autism diagnosis using transfer learning and eye-tracking. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF (256-263). https://doi.org/10.5220/0010975500003123

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.

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.

The role of text analytics in healthcare: A review of recent developments and applications (2021)
Conference Proceeding
Elbattah, M., Arnaud, É., Gignon, M., & Dequen, G. (2021). The role of text analytics in healthcare: A review of recent developments and applications. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (825-832). https://doi.org/10.5220/0010414508250832

The implementation of Data Analytics has achieved a significant momentum across a very wide range of domains. Part of that progress is directly linked to the implementation of Text Analytics solutions. Organisations increasingly seek to harness the p... Read More about The role of text analytics in healthcare: A review of recent developments and applications.

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.

NLP-based prediction of medical specialties at hospital admission using triage notes (2021)
Conference Proceeding
Arnaud, E., Elbattah, M., Gignon, M., & Dequen, G. (2021). NLP-based prediction of medical specialties at hospital admission using triage notes. In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI) (548-553). https://doi.org/10.1109/ichi52183.2021.00103

Data Analytics is rapidly expanding within the healthcare domain to help develop strategies for improving the quality of care and curbing costs as well. Natural Language Processing (NLP) solutions have received particular attention whereas a large pa... Read More about NLP-based prediction of medical specialties at hospital admission using triage notes.

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.

Generative modeling of synthetic eye-tracking data: NLP-based approach with recurrent neural networks (2020)
Conference Proceeding
Elbattah, M., Guérin, J., Carette, R., Cilia, F., & Dequen, G. (2020). Generative modeling of synthetic eye-tracking data: NLP-based approach with recurrent neural networks. In Proceedings of the 12th International Joint Conference on Computational Intelligence (479-484). https://doi.org/10.5220/0010177204790484

This study explores a Machine Learning-based approach for generating synthetic eye-tracking data. In this respect, a novel application of Recurrent Neural Networks is experimented. Our approach is based on learning the sequence patterns of eye-tracki... Read More about Generative modeling of synthetic eye-tracking data: NLP-based approach with recurrent neural networks.

Multi-channel ConvNet approach to predict the risk of in-hospital mortality for ICU patients (2020)
Conference Proceeding
Viton, F., Elbattah, M., Guérin, J., & Dequen, G. (2020). Multi-channel ConvNet approach to predict the risk of in-hospital mortality for ICU patients. In Proceedings of the 1st International Conference on Deep Learning Theory and Applications (98-102). https://doi.org/10.5220/0009891900980102

The healthcare arena has been undergoing impressive transformations thanks to advances in the capacity to capture, store, process, and learn from data. This paper re-visits the problem of predicting the risk of in-hospital mortality based on Time Ser... Read More about Multi-channel ConvNet approach to predict the risk of in-hospital mortality for ICU patients.

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.

Deep learning to predict hospitalization at triage: Integration of structured data and unstructured text (2020)
Conference Proceeding
Arnaud, E., Elbattah, M., Gignon, M., & Dequen, G. (2020). Deep learning to predict hospitalization at triage: Integration of structured data and unstructured text. In 2020 IEEE International Conference on Big Data (Big Data) (4836-4841). https://doi.org/10.1109/bigdata50022.2020.9378073

Overcrowding in Emergency Departments (ED) is considered as an international issue, which could have adverse impacts on multiple care outcomes such as the length of stay for example. Part of the solution could lie in the early prediction of the patie... Read More about Deep learning to predict hospitalization at triage: Integration of structured data and unstructured text.

NLP-based approach to detect autism spectrum disorder in saccadic eye movement (2020)
Conference Proceeding
Elbattah, M., Guerin, J. L., Carette, R., Cilia, F., & Dequen, G. (2020). NLP-based approach to detect autism spectrum disorder in saccadic eye movement. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (1581-1587). https://doi.org/10.1109/ssci47803.2020.9308238

Autism Spectrum Disorder (ASD) is a lifelong condition generally characterized by social and communication impairments. The early diagnosis of ASD is highly desirable, yet it could be complicated by several factors. Standard tests typically require i... Read More about NLP-based approach to detect autism spectrum disorder in saccadic eye movement.

Heatmaps for visual explainability of CNN-based predictions for multivariate time series with application to healthcare (2020)
Conference Proceeding
Viton, F., Elbattah, M., Guerin, J. L., & Dequen, G. (2020). Heatmaps for visual explainability of CNN-based predictions for multivariate time series with application to healthcare. In 2020 IEEE International Conference on Healthcare Informatics (ICHI)https://doi.org/10.1109/ICHI48887.2020.9374393

The need for explainable AI is becoming increasingly important for critical decision domains such as healthcare for example. In this context, this paper is concerned with explaining the predictions of Convolutional Neural Networks (CNNs) with particu... Read More about Heatmaps for visual explainability of CNN-based predictions for multivariate time series with application to healthcare.