Inferring High-Level Events from Timestamped Data: Complexity and Medical Applications

arXiv cs.AI / 4/25/2026

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Key Points

The paper introduces a logic-based method for detecting high-level, temporally extended events from timestamped data combined with background knowledge.
It models simple temporal events and composes them into meta-events by specifying existence and termination conditions, with a concrete medical example of inferring disease episodes and therapies.
To reduce incorrect inferences, the framework uses constraints to detect incompatible event combinations and applies a repair mechanism to choose consistent, preferred event sets.
Although full reasoning is intractable, the authors derive restrictions under which data complexity becomes polynomial-time, and they implement core components using answer set programming.
An evaluation on a lung cancer use case indicates the approach is computationally feasible and aligns positively with medical expert opinions, while remaining generic for reuse beyond healthcare.

Abstract

In this paper, we develop a novel logic-based approach to detecting high-level temporally extended events from timestamped data and background knowledge. Our framework employs logical rules to capture existence and termination conditions for simple temporal events and to combine these into meta-events. In the medical domain, for example, disease episodes and therapies are inferred from timestamped clinical observations, such as diagnoses and drug administrations stored in patient records, and can be further combined into higher-level disease events. As some incorrect events might be inferred, we use constraints to identify incompatible combinations of events and propose a repair mechanism to select preferred consistent sets of events. While reasoning in the full framework is intractable, we identify relevant restrictions that ensure polynomial-time data complexity. Our prototype system implements core components of the approach using answer set programming. An evaluation on a lung cancer use case supports the interest of the approach, both in terms of computational feasibility and positive alignment of our results with medical expert opinions. While strongly motivated by the needs of the healthcare domain, our framework is purposely generic, enabling its reuse in other areas.