Outputs (6)

Mining the Irish hip fracture database: Learning factors contributing to care outcomes (2020)
Journal Article

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.

Generative modeling of synthetic eye-tracking data: NLP-based approach with recurrent neural networks (2020)
Presentation / Conference Contribution

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)
Presentation / Conference Contribution

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.

Deep learning to predict hospitalization at triage: Integration of structured data and unstructured text (2020)
Presentation / Conference Contribution

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)
Presentation / Conference Contribution

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)
Presentation / Conference Contribution

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.