Recurrence Prediction

Recurrence prediction aims to forecast the likelihood of a disease or event reappearing after an initial treatment or remission, primarily to personalize patient care and optimize treatment strategies. Current research heavily utilizes machine learning, employing deep neural networks (including convolutional neural networks, recurrent neural networks like LSTMs, and Transformers), along with traditional methods like Cox proportional hazards models, to analyze diverse data types such as medical images, genomic data, and clinical records. These advancements offer the potential for more accurate risk stratification across various cancers (e.g., breast, prostate, lung, bladder, esophageal) and other conditions like seizures, leading to improved patient outcomes and more efficient resource allocation in healthcare.