3 citations · 3 across the 1 of their papers we have counts for
6 papers
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,…
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…
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…
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-…
Development and Comparative Analysis of Machine Learning Models for Hypoxemia Severity Triage in CBRNE Emergency Scenarios Using Physiological and Demographic Data from Medical-Grade Devices
Santino Nanini, Mariem Abid, Yassir Mamouni +3
This paper presents the development of machine learning (ML) models to predict hypoxemia severity during emergency triage, especially in Chemical, Biological, Radiological, Nuclear…