Introduction Multimodal data in healthcare integrates diverse sources, such as medical imaging, wearable sensor readings, genomic information, and electronic health records (EHRs), offering profound insights for accurate diagnoses and treatment. However, missing data remains a… The Potentials and Challenges of Handling Missing Data in Multimodal Healthcare Data
Brief summary: While PCA is traditionally employed for dimensionality reduction and denoising before training, this preprocessing can complicate the interpretability of explainable AI (XAI) methods due to the transformation of input features. To mitigate these… Principal Components for Neural Network Initialization: A Novel Approach to Explainability and Efficiency
The paper “DPER: Direct Parameter Estimation for Randomly Missing Data,” by Thu Nguyen, Khoi Minh Nguyen-Duy, Duy Ho Minh Nguyen, Binh T. Nguyen, Bruce Alan Wade introduces a novel methodology for handling missing data. Its main contributions are as follows: These contributions position the DPER… DPER: Direct Parameter Estimation for Randomly Missing Data
Detailed information can be found in Combining datasets to improve model fitting or its presentation slide. Summary: The key points of the paper titled “Combining Datasets to Improve Model Fitting” are as follows: Problem and… Combining datasets to increase sample size