Predicting the survival status of Intensive Care patients at the end of their hospital stay is useful for various clinical and organizational tasks. Current models for predicting mortality use logistic regression models that rely solely on data collected during the first 24h of patient admission. These models do not exploit information contained in daily organ failure scores which nowadays are being routinely collected in many Intensive Care Units. We propose a novel method for mortality prediction that, in addition to admission-related data, takes advantage of daily data as well. The method is characterized by the data-driven discovery of temporal patterns, called episodes, of the organ failure scores and by embedding them in the familiar logistic regression framework for prediction. Our method results in a set of D logistic regression models, one for each of the first D days of Intensive Care Unit stay. A model for day d<or=D is trained on the patient subpopulation that stayed at least d days in the Intensive Care Unit and predicts the probability of death at the end of hospital stay for such patients. We implemented our method, with a specific form of episodes, called aligned episodes, on a large dataset of Intensive Care Unit patients for the first 5 days of stay (D=5) in the unit. We compared our models with ones that were developed on the same patient subpopulations but which did not use the episodes. The new models show improved performance on each of the five days. They also provide insight in the effect of the various selected episodes on mortality.