1. Data Collection: Gathering data from various sources like EHRs, patient surveys, and clinical trials. 2. Data Cleaning: Ensuring the data is accurate and free of errors. 3. Model Building: Using algorithms like regression analysis, decision trees, and machine learning to create models. 4. Validation: Testing the model with a separate dataset to ensure its accuracy. 5. Implementation: Integrating the model into clinical workflows to assist in decision-making.
