65 citations · 113 across the 15 of their papers we have counts for
15 papers
Non-Contact Physiological Monitoring in Pediatric Intensive Care Units via Adaptive Masking and Self-Supervised Learning
Mohamed Khalil Ben Salah, Philippe Jouvet, Rita Noumeir
Continuous monitoring of vital signs in Pediatric Intensive Care Units (PICUs) is essential for early detection of clinical deterioration and effective clinical decision-making. Ho…
Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment
Aamer Abdul Rahman, Pranav Agarwal, Rita Noumeir +3
Offline reinforcement learning has shown promise for solving tasks in safety-critical settings, such as clinical decision support. Its application, however, has been limited by the…
The Impact of LoRA Adapters on LLMs for Clinical Text Classification Under Computational and Data Constraints
Thanh-Dung Le, Ti Ti Nguyen, Vu Nguyen Ha +3
Fine-tuning Large Language Models (LLMs) for clinical Natural Language Processing (NLP) poses significant challenges due to domain gap, limited data, and stringent hardware constra…
Transformer Meets Gated Residual Networks To Enhance Photoplethysmogram Artifact Detection Informed by Mutual Information Neural Estimation
Thanh-Dung Le, Clara Macabiau, Kévin Albert +3
This study delves into the effectiveness of various learning methods in improving Transformer models, focusing particularly on the Gated Residual Network Transformer (GRN-Transform…
Heart Rate and Body Temperature Relationship in Children Admitted to PICU -- A Machine Learning Approach
Emilie Lu, Thanh-Dung Le
Vital signs have been essential clinical measures. Among these, body temperature (BT) and heart rate (HR) are particularly significant, and numerous studies explored their associat…
A Novel Transformer-Based Self-Supervised Learning Method to Enhance Photoplethysmogram Signal Artifact Detection
Thanh-Dung Le, Clara Macabiau, Kévin Albert +2
Recent research at CHU Sainte Justine's Pediatric Critical Care Unit (PICU) has revealed that traditional machine learning methods, such as semi-supervised label propagation and K-…
Label Propagation Techniques for Artifact Detection in Imbalanced Classes using Photoplethysmogram Signals
Clara Macabiau, Thanh-Dung Le, Kevin Albert +3
This study aimed to investigate the application of label propagation techniques to propagate labels among photoplethysmogram (PPG) signals, particularly in imbalanced class scenari…
GRN-Transformer: Enhancing Motion Artifact Detection in PICU Photoplethysmogram Signals
Thanh-Dung Le, Clara Macabiau, Kévin Albert +2
Photoplethysmogram (PPG) signals, optical measurements of pulsatile blood flow used continuously in intensive care monitoring, are frequently contaminated by motion, low perfusion,…
Improving Transformer Performance for French Clinical Notes Classification Using Mixture of Experts on a Limited Dataset
Thanh-Dung Le, Philippe Jouvet, Rita Noumeir
Transformer-based models have shown outstanding results in natural language processing but face challenges in applications like classifying small-scale clinical texts, especially w…
Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes Representation Learning
Thanh-Dung Le, Rita Noumeir, Jerome Rambaud +2
When dealing with clinical text classification on a small dataset recent studies have confirmed that a well-tuned multilayer perceptron outperforms other generative classifiers, in…
Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation
Thanh-Dung Le, Rita Noumeir, Jerome Rambaud +2
The rapid progress in clinical data management systems and artificial intelligence approaches enable the era of personalized medicine. Intensive care units (ICUs) are the ideal cli…
Machine Learning Based on Natural Language Processing to Detect Cardiac Failure in Clinical Narratives
Thanh-Dung Le, Rita Noumeir, Jerome Rambaud +2
The purpose of the study presented herein is to develop a machine learning algorithm based on natural language processing that automatically detects whether a patient has a cardiac…
Bridging the gap between Human Action Recognition and Online Action Detection
Alban Main de Boissiere, Rita Noumeir
Action recognition, early prediction, and online action detection are complementary disciplines that are often studied independently. Most online action detection networks use a pr…
Infrared and 3D skeleton feature fusion for RGB-D action recognition
Alban Main de Boissiere, Rita Noumeir
A challenge of skeleton-based action recognition is the difficulty to classify actions with similar motions and object-related actions. Visual clues from other streams help in that…
Critical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network Approach
Thanh Dung Le, Rita Noumeir, Huu Luong Quach +3
Much research in recent years has focused on using empirical machine learning approaches to extract useful insights on the structure-property relationships of superconductor materi…