Developing a prognostic model involves several steps:
Data Collection: Gathering comprehensive data from patient records, clinical trials, and other sources. Feature Selection: Identifying the most relevant variables that influence outcomes. Model Training: Using statistical methods or machine learning algorithms to build the model. Validation: Testing the model on independent datasets to assess its accuracy and generalizability.