Disease Trajectory

Disease trajectory research focuses on understanding and predicting the temporal evolution of diseases, aiming to improve diagnosis, prognosis, and treatment personalization. Current research employs diverse machine learning models, including transformers, generative models, and stochastic differential equations, to analyze complex, often irregularly sampled, patient data (e.g., medical images, electronic health records) and identify meaningful disease subtypes and progression patterns. These advancements enable more accurate predictions of disease progression, facilitating personalized medicine and the development of targeted interventions. The ultimate goal is to leverage these insights to improve patient outcomes and optimize healthcare resource allocation.