Disease Progression

Disease progression modeling aims to predict the future course of a disease based on various data sources, including medical images and electronic health records, to improve diagnosis, treatment, and patient management. Current research emphasizes the use of deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and neural ordinary differential equations (NODEs), often incorporating self-supervised learning and techniques to handle data heterogeneity and uncertainty. These advancements are improving the accuracy of disease progression predictions across various conditions, leading to more personalized and effective healthcare interventions. The development of interpretable models is also a key focus, enabling clinicians to understand the factors driving disease progression and facilitating more informed decision-making.