Healthcare Trajectory Mining by Combining Multidimensional Component and Itemsets

1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
2 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
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 multidimensional items. However, in real-world scenarios, data sequences are described as events of both multidimensional items and set valued information. These rich heterogeneous descriptions cannot be exploited by traditional approaches. For example, in healthcare domain, hospitalizations are defined as sequences of multi-dimensional attributes (e.g. Hospital or Diagnosis) associated with two sets, set of medical procedures (e.g. $\lbrace$ Radiography, Appendectomy $\rbrace$) and set of medical drugs (e.g. $\lbrace$ Aspirin, Paracetamol $\rbrace$) . In this paper we propose a new approach called MMISP ({\it Mining Multidimensional Itemset Sequential Patterns}) to extract patterns from a complex sequences including both dimensional items and itemsets. The novelties of the proposal lies in: (i) the way in which the data can be efficiently compressed; (ii) the ability to reuse and adopt sequential pattern mining algorithms and (iii) the extraction of new kind of patterns. We introduce as a case-study, experimented on real data aggregated 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 our approach.
Type de document :
Chapitre d'ouvrage
Annalisa Appice. New Frontiers in Mining Complex Patterns - First International Workshop, NFMCP 2012, Held in Conjunction with ECML/PKDD 2012, Springer, 2012
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https://hal.inria.fr/hal-00922512
Contributeur : Elias Egho <>
Soumis le : vendredi 27 décembre 2013 - 12:56:07
Dernière modification le : jeudi 11 janvier 2018 - 06:26:17
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• HAL Id : hal-00922512, version 1

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Elias Egho, Chedy Raïssi, Dino Ienco, Nicolas Jay, Amedeo Napoli, et al.. Healthcare Trajectory Mining by Combining Multidimensional Component and Itemsets. Annalisa Appice. New Frontiers in Mining Complex Patterns - First International Workshop, NFMCP 2012, Held in Conjunction with ECML/PKDD 2012, Springer, 2012. 〈hal-00922512〉

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