Towards a general theory of action and time
Artificial Intelligence
A framework for knowledge-based temporal abstraction
Artificial Intelligence
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Representing and Reasoning about Temporal Granularities
Journal of Logic and Computation
Artificial Intelligence in Medicine
Data mining with Temporal Abstractions: learning rules from time series
Data Mining and Knowledge Discovery
Temporal data mining for the quality assessment of hemodialysis services
Artificial Intelligence in Medicine
The socio-organizational age of artificial intelligence in medicine
Artificial Intelligence in Medicine
Using data mining techniques to predict hospitalization of hemodialysis patients
Decision Support Systems
Discovering metric temporal constraint networks on temporal databases
Artificial Intelligence in Medicine
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The Regional Healthcare Agency (ASL) of Pavia has been maintaining a central data repository which stores healthcare data about the population of Pavia area. The analysis of such data can be fruitful for the assessment of healthcare activities. Given the crucial role of time in such databases, we developed a general methodology for the mining of Temporal Association Rules on sequences of hybrid events. In this paper we show how the method can be extended to suitably manage the integration of both clinical and administrative data. Moreover, we address the problem of developing an automated strategy for the filtering of output rules, exploiting the taxonomy underlying the drug coding system and considering the relationships between clinical variables and drug effects. The results show that the method could find a practical use for the evaluation of the pertinence of the care delivery flow for specific pathologies.