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All Outputs (6)

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.