Early Prognosis

Early prognosis research aims to predict disease progression and outcomes, enabling timely interventions and personalized treatment strategies. Current efforts leverage machine learning, employing diverse models like ensemble methods (e.g., Random Forests), deep learning architectures (e.g., transformer networks), and shallow learning algorithms (e.g., Extremely Randomized Trees), often incorporating multi-modal data such as medical images and biomedical signals. This work holds significant promise for improving patient care across various diseases, from chronic kidney disease to cancer, by facilitating earlier and more accurate risk stratification and treatment planning. The ability to predict disease progression using readily available clinical data could revolutionize healthcare resource allocation and improve patient outcomes.