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

2026-04-23

AI Summary

This work addresses the challenge of accurately identifying clinically meaningful high-order composite events—such as disease onset—from timestamped raw data. The authors propose a logic-rule-based framework that constructs high-order events by combining existence and termination conditions of atomic events, augmented with consistency constraints and a repair mechanism to eliminate invalid combinations. Innovatively integrating logical rules, temporal reasoning, and constraint optimization, the approach identifies—for the first time—a computationally tractable fragment that balances expressive power with polynomial-time data complexity. Implemented via answer set programming, the core inference module demonstrates feasibility in a lung cancer case study, yielding results highly consistent with clinical expert judgments and highlighting its potential for healthcare and other temporal data analysis applications.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance

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.

Problem

• high-level events
• timestamped data
• temporal reasoning
• event inference
• medical applications

Innovation

• logic-based event inference
• temporal reasoning
• answer set programming
• event consistency repair
• polynomial data complexity