SADC

Regroupement en IA
soins aigus pour l'enfant

Les articles du laboratoire

Le laboratoire croit en une vision basée sur un modèle d’innovation ouverte pour les membres du groupe et partiellement ouverte ou contrôlé à l’extérieur du groupe. Dans cette optique, nous souhaitons partager nos connaissances et faire rayonner le milieu académique avec la publication d'articles.

Nos articles
 

Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes Representation Learning

Année de parution: 2023

When dealing with clinical text classification on a small dataset recent studies have confirmed that a well-tuned multilayer perceptron outperforms other generative classifiers, including deep learning ones. To increase the performance of the neural network classifier, feature selection for the learning representation can effectively be used.

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Clinical Decision Support in the PICU: Implications for Design and Evaluation

Année de parution: 2022

To assess the current landscape of clinical decision support (CDS) tools in PICUs in order to identify priority areas of focus in this field.

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A data-driven approach to include availability of ICU beds in the planning of the operating roomR

Année de parution: 2022

In this paper, we propose a novel approach to deal with the integration of the cancellation probability due to congestion in the intensive care unit in the (long term) surgical case assignment problem.

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Optical Thermography Infrastructure to Assess Thermal Distribution in Critically Ill Children

Année de parution: 2022

The temperature distribution at the skin surface could be a useful tool to monitor changes in cardiac output. Goal: The aim of this study was to explore infrared thermography as a method to analyze temperature profiles of critically ill children. Methods: Patients admitted to the pediatric intensive care unit (PICU) were included in this study. An infrared sensor was used to take images in clinical conditions.

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Detecting of a Patient’s Condition From Clinical Narratives Using Natural Language Representation

Année de parution: 2022

The study is a showcase to confirm that, dealing with a small dataset of clinical notes, a multilayer perceptron neural network classifier is a better approach compared to conventional classifiers, especially, pretrained-based deep learning models. Additionally, instead of losing information from numeric values, they can be retained and encoded for the representation learning. Consequently, it achieves better results for the classification task.

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Remote design of a pediatric intensive care unit dashboard in time of pandemics

Année de parution: 2021

To support the pediatric intensive care unit with the COVID-19 pandemic, we followed a user-centered design process to create a dashboard in a context where direct access to users was impossible. To this end, we applied contextual inquiry, user interview, requirement definition, iterative design with user validation and usability testing in a remote fashion.

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Dense-Unet: a light model for lung fields segmentation in Chest X-Ray images

Année de parution: 2020

Automatic and accurate lung segmentation in chest X-ray (CXR) images is fundamental for computer-aided diagnosis systems since the lung is the region of interest in many diseases and also it can reveal useful information by its contours.

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Automatic eye localization for hospitalized infants and children using convolutional neural networks

Année de parution: 2020

Reliable localization and tracking of the eye region in the pediatric hospital environment is a significant challenge for clinical decision support and patient monitoring applications. Existing work in eye localization achieves high performance on adult datasets but performs poorly in the busy pediatric hospital environment, where face appearance varies because of age, position and the presence of medical equipment.

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