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

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

doi:10.1007/978-3-642-02976-9_3

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

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

Istituti Clinici Scientifici Maugeri Spa – Società Benefit

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 language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages	16-25
Number of pages	10
Volume	5651 LNAI
DOIs	https://doi.org/10.1007/978-3-642-02976-9_3
Publication status	Published - 2009
Event	12th Conference on Artificial Intelligence in Medicine, AIME 2009 - Verona, Italy Duration: Jul 18 2009 → Jul 22 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5651 LNAI
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Other

Other	12th Conference on Artificial Intelligence in Medicine, AIME 2009
Country	Italy
City	Verona
Period	7/18/09 → 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

Access to Document

10.1007/978-3-642-02976-9_3

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Cite this

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

Mining healthcare data with temporal association rules : Improvements and assessment for a practical use. / Concaro, Stefano; Sacchi, Lucia; Cerra, Carlo; Fratino, Pietro; Bellazzi, Riccardo.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5651 LNAI 2009. p. 16-25 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5651 LNAI).

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

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, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5651 LNAI, pp. 16-25, 12th Conference on Artificial Intelligence in Medicine, AIME 2009, Verona, Italy, 7/18/09. https://doi.org/10.1007/978-3-642-02976-9_3

Concaro S, Sacchi L, Cerra C, Fratino P, Bellazzi R. 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. 2009. p. 16-25. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-02976-9_3

Concaro, Stefano ; Sacchi, Lucia ; Cerra, Carlo ; Fratino, Pietro ; Bellazzi, Riccardo. / Mining healthcare data with temporal association rules : Improvements and assessment for a practical use. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5651 LNAI 2009. pp. 16-25 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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