Publications by authors named "Pierre-Alexandre Mattei"
Article Synopsis
- Aortic valve stenosis (AS) is a chronic disease that progresses at different rates among patients, making it challenging to predict its progression.* -
- This study utilized machine and deep learning algorithms on data from 303 patients to forecast AS progression over the next 2 and 5 years, showing that the LightGBM model yielded the best predictive performance.* -
- The findings suggest that using AI in clinical settings can improve the risk assessment of AS, effectively predicting the disease progression and outcomes for patients with mild-to-moderate AS.*
In supervised classification problems, the test set may contain data points belonging to classes not observed in the learning phase. Moreover, the same units in the test data may be measured on a set of additional variables recorded at a subsequent stage with respect to when the learning sample was collected. In this situation, the classifier built in the learning phase needs to adapt to handle potential unknown classes and the extra dimensions.
View Article and Find Full Text PDF