Paper Info
Title
A contribution to the discovery of multidimensional patterns in healthcare trajectories.
Abstract
Sequential pattern mining is aimed at extracting correlations among temporal data. Many different methods were proposed to either enumerate sequences of set valued data (i.e., itemsets) or sequences containing dimensional items. However, in real-world scenarios, data sequences are described as combination of both multidimensional items and itemsets. These heterogeneous descriptions cannot be handled by traditional approaches. In this paper we propose a new approach called MMISP () to extract patterns from complex sequential database including both multidimensional items and itemsets. The novelties of the proposal lies in: (i) the way in which the data are efficiently compressed; (ii) the ability to reuse and adopt sequential pattern mining algorithms and (iii) the extraction of new kind of patterns. We introduce a case-study on real-world data from a regional healthcare system and we point out the usefulness of the extracted patterns. Additional experiments on synthetic data highlights the efficiency and scalability of the approach .
Year
DOI
Venue
2014
10.1007/s10844-014-0309-4
J. Intell. Inf. Syst.
Keywords
Field
DocType
Complex sequential patterns,Multidimensional sequential patterns,Data mining,Complex data
Data mining,Computer science,Reuse,Complex data type,Temporal database,Synthetic data,Data sequences,Artificial intelligence,Healthcare system,Sequential Pattern Mining,Machine learning,Scalability
Journal
Volume
Issue
ISSN
42
2
0925-9902
Citations 
PageRank 
References 
3
0.42
17
Authors
7
Name
Order
Citations
PageRank
Elias Egho1616.05
Nicolas Jay211813.26
Chedy Raïssi320321.76
Dino Ienco429542.01
Pascal Poncelet5768126.47
Maguelonne Teisseire6557129.00
Amedeo Napoli71180135.52