Inferring High-Level Events from Timestamped Data: Complexity and Medical Applications
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...
| Authors | Yvon K. Awuklu, Meghyn Bienvenu, Katsumi Inoue et al. |
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| Primary category | cs.AI |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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