All Outputs (27)

Exploring the ethical challenges of large language models in emergency medicine: A comparative international review (2025)
Presentation / Conference Contribution

Large Language Models (LLMs) hold promise for advancing Emergency Medicine by enhancing operational efficiency and supporting decision-making. This scoping review explores the ethical, legal, and global considerations influencing LLM deployment in em... Read More about Exploring the ethical challenges of large language models in emergency medicine: A comparative international review.

Editorial: Improving autism spectrum disorder diagnosis using machine learning techniques (2024)
Journal Article

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social communication, repetitive behaviors, and restricted interests (American Psychiatric Association, 2013). Early and accurate diagnosis is cri... Read More about Editorial: Improving autism spectrum disorder diagnosis using machine learning techniques.

Consistency of XAI models against medical expertise: An assessment protocol (2024)
Presentation / Conference Contribution

Despite the significant advances made by Artificial Intelligence (AI) models in enhancing medical diagnostics and prognostics, their opacity poses a hurdle to widespread clinical adoption. In this regard, Explainable AI (XAI) aims to demystify these... Read More about Consistency of XAI models against medical expertise: An assessment protocol.

Explainable NLP model for predicting patient admissions at emergency department using triage notes (2024)
Presentation / Conference Contribution

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

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

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.

Vision-based approach for autism diagnosis using transfer learning and eye-tracking (2022)
Presentation / Conference Contribution

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.

Eye-tracking dataset to support the research on autism spectrum disorder (2022)
Presentation / Conference Contribution

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.

Learning embeddings from free-text triage notes using pretrained transformer models (2022)
Presentation / Conference Contribution

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.

Use of artificial intelligence to manage patient flow in emergency department during the COVID-19 pandemic: A prospective, single-center study (2022)
Journal Article

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)
Presentation / Conference Contribution

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

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)
Presentation / Conference Contribution

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

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)
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.

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.

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.