Mining healthcare data with temporal association rules: Improvements and assessment for a practical use

Stefano Concaro, Lucia Sacchi, Carlo Cerra, Pietro Fratino, Riccardo Bellazzi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages16-25
Number of pages10
Volume5651 LNAI
DOIs
Publication statusPublished - 2009
Event12th Conference on Artificial Intelligence in Medicine, AIME 2009 - Verona, Italy
Duration: Jul 18 2009 → Jul 22 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5651 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other12th Conference on Artificial Intelligence in Medicine, AIME 2009
CountryItaly
CityVerona
Period7/18/09 → 7/22/09

Keywords

  • Diabetes mellitus
  • Healthcare data
  • Hybrid events
  • Temporal association rules
  • Temporal data mining

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5651 LNAI, pp. 16-25). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5651 LNAI). https://doi.org/10.1007/978-3-642-02976-9_3