A healthcare provider trained a model on historical patient records (medical history, demographics, treatments) to deliver real-time predictions of 30-day readmission risk after discharge. Which activity best describes model inference in this scenario?

Model inference is the process of using a trained machine learning model to make predictions or decisions on new, unseen data. In this scenario, applying the trained model to predict whether a patient will be readmitted directly aligns with this definition. Collecting historical patient readmission records is part of data collection, a precursor to model training. Evaluating model performance using appropriate metrics occurs after training and is part of the model validation phase. Analyzing data to discover patient patterns and correlations is part of data exploration and feature engineering, which also precede or are part of model training. Only applying the already trained model to new patient data to generate a prediction constitutes inference.