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 | |
| 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 |
Fingerprint
Keywords
- Diabetes mellitus
- Healthcare data
- Hybrid events
- Temporal association rules
- Temporal data mining
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
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
}
TY - GEN
T1 - Mining healthcare data with temporal association rules
T2 - Improvements and assessment for a practical use
AU - Concaro, Stefano
AU - Sacchi, Lucia
AU - Cerra, Carlo
AU - Fratino, Pietro
AU - Bellazzi, Riccardo
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Diabetes mellitus
KW - Healthcare data
KW - Hybrid events
KW - Temporal association rules
KW - Temporal data mining
UR - http://www.scopus.com/inward/record.url?scp=70350214798&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350214798&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-02976-9_3
DO - 10.1007/978-3-642-02976-9_3
M3 - Conference contribution
AN - SCOPUS:70350214798
SN - 3642029752
SN - 9783642029752
VL - 5651 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 16
EP - 25
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -