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2020 – today
- 2024
[c25]Émilien Arnaud, Mahmoud Elbattah, Amandine Pitteman, Gilles Dequen, Daniel Aiham Ghazali, Pedro A. Moreno-Sánchez:
Consistency of XAI Models against Medical Expertise: An Assessment Protocol. ICHI 2024: 732-736- 2023
- [j2]Émilien Arnaud
, Mahmoud Elbattah, Christine Ammirati, Gilles Dequen, Daniel Aiham Ghazali:
Predictive models in emergency medicine and their missing data strategies: a systematic review. npj Digit. Medicine 6 (2023) - [c24]Émilien Arnaud, Mahmoud Elbattah, Pedro A. Moreno-Sánchez, Gilles Dequen, Daniel Aiham Ghazali:
Explainable NLP Model for Predicting Patient Admissions at Emergency Department Using Triage Notes. IEEE Big Data 2023: 4843-4847 - 2022
- [c23]Mahmoud Elbattah, Jean-Luc Guérin, Romuald Carette, Federica Cilia, Gilles Dequen:
Vision-based Approach for Autism Diagnosis using Transfer Learning and Eye-tracking. HEALTHINF 2022: 256-263 - [c22]Émilien Arnaud, Mahmoud Elbattah, Maxime Gignon, Gilles Dequen:
Learning Embeddings from Free-text Triage Notes using Pretrained Transformer Models. HEALTHINF 2022: 835-841 - 2021
- [j1]Mahmoud Elbattah, Colm Loughnane, Jean-Luc Guérin, Romuald Carette, Federica Cilia, Gilles Dequen:
Variational Autoencoder for Image-Based Augmentation of Eye-Tracking Data. J. Imaging 7(5): 83 (2021) - [c21]Mahmoud Elbattah, Émilien Arnaud, Maxime Gignon, Gilles Dequen:
The Role of Text Analytics in Healthcare: A Review of Recent Developments and Applications. HEALTHINF 2021: 825-832 - [c20]Émilien Arnaud, Mahmoud Elbattah, Maxime Gignon, Gilles Dequen:
NLP-Based Prediction of Medical Specialties at Hospital Admission Using Triage Notes. ICHI 2021: 548-553 - 2020
- [c19]Émilien Arnaud, Mahmoud Elbattah, Maxime Gignon, Gilles Dequen:
Deep Learning to Predict Hospitalization at Triage: Integration of Structured Data and Unstructured Text. IEEE BigData 2020: 4836-4841 - [c18]Fabien Viton, Mahmoud Elbattah, Jean-Luc Guérin, Gilles Dequen:
Multi-channel ConvNet Approach to Predict the Risk of in-Hospital Mortality for ICU Patients. DeLTA 2020: 98-102 - [c17]Fabien Viton, Mahmoud Elbattah, Jean-Luc Guérin, Gilles Dequen:
Heatmaps for Visual Explainability of CNN-Based Predictions for Multivariate Time Series with Application to Healthcare. ICHI 2020: 1-8 - [c16]Mahmoud Elbattah, Jean-Luc Guérin, Romuald Carette, Federica Cilia, Gilles Dequen:
Generative Modeling of Synthetic Eye-tracking Data: NLP-based Approach with Recurrent Neural Networks. IJCCI 2020: 479-484 - [c15]Mahmoud Elbattah, Jean-Luc Guérin, Romuald Carette, Federica Cilia, Gilles Dequen:
NLP-Based Approach to Detect Autism Spectrum Disorder in Saccadic Eye Movement. SSCI 2020: 1581-1587
2010 – 2019
- 2019
- [c14]Romuald Carette, Mahmoud Elbattah, Federica Cilia, Gilles Dequen, Jean-Luc Guérin, Jérôme Bosche:
Learning to Predict Autism Spectrum Disorder based on the Visual Patterns of Eye-tracking Scanpaths. HEALTHINF 2019: 103-112 - [c13]Mahmoud Elbattah:
How can Machine Learning Support the Practice of Modeling and Simulation? - A Review and Directions for Future Research. DS-RT 2019: 1-7 - [c12]Mahmoud Elbattah, Romuald Carette, Gilles Dequen, Jean-Luc Guérin, Federica Cilia:
Learning Clusters in Autism Spectrum Disorder: Image-Based Clustering of Eye-Tracking Scanpaths with Deep Autoencoder. EMBC 2019: 1417-1420 - [c11]Mahmoud Elbattah, Conor Ryan:
Learning Triple Sequence Patterns in Knowledge Graphs to Predict Inconsistencies. KMIS 2019: 435-441 - 2018
- [c10]Romuald Carette, Mahmoud Elbattah, Gilles Dequen, Jean-Luc Guérin, Federica Cilia:
Visualization of Eye-Tracking Patterns in Autism Spectrum Disorder: Method and Dataset. ICDIM 2018: 248-253 - [c9]Mahmoud Elbattah, Owen Molloy:
ML-Aided Simulation: A Conceptual Framework for Integrating Simulation Models with Machine Learning. SIGSIM-PADS 2018: 33-36 - [c8]Mahmoud Elbattah, Owen Molloy, Bernard P. Zeigler:
Designing Care pathways using simulation Modeling and Machine Learning. WSC 2018: 1452-1463 - 2017
- [c7]Mahmoud Elbattah, Owen Molloy:
Clustering-Aided Approach for Predicting Patient Outcomes with Application to Elderly Healthcare in Ireland. AAAI Workshops 2017 - [c6]Mahmoud Elbattah, Owen Molloy:
Data-driven patient segmentation using K-means clustering: the case of hip fracture care in Ireland. ACSW 2017: 60:1-60:8 - [c5]Mahmoud Elbattah, Owen Molloy:
Learning about systems using machine learning: Towards more data-driven feedback loops. WSC 2017: 1539-1550 - [c4]Mahmoud Elbattah:
ScholarBase: Towards a Cross-Domain Knowledgebase for Linked Scholarly Data. SWM@WSDM 2017: 33-40 - 2016
- [c3]Mahmoud Elbattah, Owen Molloy:
Using Machine Learning to Predict Length of Stay and Discharge Destination for Hip-Fracture Patients. IntelliSys (1) 2016: 207-217 - [c2]Mahmoud Elbattah, Owen Molloy:
Coupling Simulation with Machine Learning: A Hybrid Approach for Elderly Discharge Planning. SIGSIM-PADS 2016: 47-56 - 2015
- [c1]Mahmoud Elbattah, Owen Molloy:
Towards Improving Modeling and Simulation of Clinical Pathways: Lessons Learned and Future Insights. SIMULTECH 2015: 508-514
Coauthor Index
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