Data and Decision Fusion with Uncertainty Quantification for ML-based Healthcare Decision Systems
Grigor Bezirganyan · 2023
This paper outlines the PhD research plan to develop a comprehensive, uncertainty-aware, multimodal deep learning approach to be used in the healthcare domain.The goal is to design a multimodal deep learning framework that can leverage the complex interconnections between various modalities in order to generate highly precise predictions for intricate healthcare datasets.In addition, the framework should also incorporate methods for quantifying and dealing with uncertainty, which is an important consideration in many real-world healthcare applications.The approach will be tested on real-world multimodal datasets from Marseilles hospitals in France.We further represent some preliminary results of our early stage experiments with uncertainty quantification on multimodal datasets.