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Found 7 papers, 1 papers with code
Label Propagation Techniques for Artifact Detection in Imbalanced Classes using Photoplethysmogram Signals
With a precision of 91%, a recall of 90% and an F1 score of 90% for the class without artifacts, the results demonstrate its effectiveness in labeling a medical dataset, even when clean samples are rare.
A Small-Scale Switch Transformer and NLP-based Model for Clinical Narratives Classification
In this study, we propose a simplified Switch Transformer framework and train it from scratch on a small French clinical text classification dataset at CHU Sainte-Justine hospital.
Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes Representation Learning
Goal: Our aim is therefore to access an alternative approach to tackle the sparsity by compressing the clinical representation feature space, where limited French clinical notes can also be dealt with effectively.
Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation
This study successfully applied learning representation and machine learning algorithms to detect heart failure from clinical natural language in a single French institution.
Machine Learning Based on Natural Language Processing to Detect Cardiac Failure in Clinical Narratives
In the case without any feature selection, the proposed framework yielded an overall classification performance with acc, pre, rec, and f1 of 84% and 82%, 85%, and 83%, respectively.
Bridging the gap between Human Action Recognition and Online Action Detection
Moreover, our networks use infrared from RGB-D cameras, which we are the first to use for online action detection, to our knowledge.
Infrared and 3D skeleton feature fusion for RGB-D action recognition
Ablation studies show that using pre-trained networks on other large scale datasets as our modules and data augmentation yield considerable improvements on the action classification accuracy.
Ranked #18 on
Action Recognition
on NTU RGB+D
