We use machine learning tools to build various types of practical models of data. This can range from predictive models that aim to identify some interesting aspect of patient data (e.g. is a monitor alert real or artifact, are there signs of disease or not), explanatory models (e.g. what differentiates one cohort from another, or one state from another), forecasting and trending models (e.g. what is going to happen in the future, will a patient become unstable), and grouping (or clustering) entities (e.g. these patients are similar to those ones).