We discuss the problem of predicting individual patient status in intensive care. While there is massive amount of data available from many medical centers, their integration for a particular intensive care unit or individual patient is challenging. We describe conceptual modeling paradigms for generalization and domain adaptation to new units, combining Empirical Bayes methods and Causal-Inspired Machine Learning. Empirical validations of such approaches provide some interesting insight.