Abstract
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.
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Concaro, S., Sacchi, L., Cerra, C., Fratino, P., Bellazzi, R. (2009). Mining Healthcare Data with Temporal Association Rules: Improvements and Assessment for a Practical Use. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds) Artificial Intelligence in Medicine. AIME 2009. Lecture Notes in Computer Science(), vol 5651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02976-9_3
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DOI: https://doi.org/10.1007/978-3-642-02976-9_3
Publisher Name: Springer, Berlin, Heidelberg
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