Predictive models typically involve the following steps:
Data Collection: Gathering historical data from electronic health records (EHRs), patient surveys, and other sources. Data Preprocessing: Cleaning and organizing the data to ensure it is suitable for analysis. Model Training: Using machine learning algorithms to train the model on the preprocessed data. Validation and Testing: Evaluating the model's performance on a separate dataset to ensure its accuracy and reliability. Implementation: Integrating the model into clinical workflows and monitoring its performance over time.
