We give a brief overview of our recent work on developing a novel logic-based framework for inferring high-level temporally extended events from timestamped clinical data and background knowledge. The framework specifies existence and termination conditions for simple events and derives meta-events by combining them. To address data imperfections, we introduce confidence annotations, consistency constraints, and a repair mechanism that selects preferred consistent event sets. While reasoning in the general setting is intractable, we identify useful fragments with polynomial-time data complexity. A prototype system, CASPER, implements the approach using answer set programming. Applied to two healthcare use cases, CASPER achieved feasible runtimes and produced clinically plausible inferences, demonstrating both computational feasibility and medical relevance. Although developed for healthcare applications, the framework is generic and can be reused in other domains.