Standard modeling and evaluation methods have been classically used in analyzing engineering dynamical systems where the fundamental problem is to minimize the (mean) error between the real and predicted systems. Although these methods have been applied to multi-step ahead predictions of physiological signals, it is often more important to predict clinically relevant events than just to match these signals. Adverse clinical events, which occur after a physiological signal breaches a clinically defined critical threshold, are a popular class of such events. This paper presents a framework for multi-step ahead predictions of critical levels of abnormality in physiological signals. First, a performance metric is presented for evaluating multi-step ahead predictions. Then, this metric is used to identify personalized models optimized with respect to predictions of critical levels of abnormality. To address the paucity of adverse events, weighted support vector machines and cost-sensitive learning are used to optimize the proposed framework with respect to statistical metrics that can take into account the relative rarity of such events.