Survival Prediction Model

Survival prediction models aim to forecast the time until a specific event, such as death or disease progression, using patient data. Current research emphasizes improving accuracy and interpretability by incorporating diverse data sources, including structured electronic health records, unstructured text (e.g., radiology reports), medical images (e.g., whole slide images, CT scans), and even facial photographs, and employing advanced machine learning techniques like deep learning (including convolutional neural networks and graph convolutional networks) and large language models. These advancements hold significant promise for personalized medicine, enabling more accurate risk stratification and improved treatment decisions across various diseases, particularly cancer and critical care. The field is also actively developing and refining evaluation metrics to better assess model performance in the presence of censored data.