Healtcare Trajectory Mining by Combining Multi-dimensional Component and Itemsets

Elias Egho 1 Dino Ienco 2 Nicolas Jay 1 Amedeo Napoli 1 Pascal Poncelet 2 Catherine Quantin 3 Chedy Raïssi 1 Maguelonne Teisseire 2
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 an approach to extract corre- lations 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 many real-world scenar- ios, data sequences are described as events of both multi-dimensional and set valued informations. These rich heterogeneous descriptions can- not 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 sets of medical procedures (e.g. { Radiography, Appendectomy }). In this paper we pro- pose a new approach called MMISP (Mining Multi-dimensional-Itemset Sequential Patterns) to extract patterns from sequences including both multi-dimensional and set valued data. The novelties of the proposal lies in: (i) the way in which the data can be efficiently compressed; (ii) the ability to reuse a state-of-the-art sequential pattern mining algo- rithm and (iii) the extraction of new kind of patterns. We introduce as a case-study, experiments on real data aggregated from a regional health- care 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 :
Communication dans un congrès
NFMCP: New Frontiers in Mining Complex Patterns, Sep 2012, Bristol, United Kingdom. Springer, New Frontiers in Mining Complex Patterns, Workshop in conjunction with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2012), LNCS (7765), 2013, 〈http://www.ecmlpkdd2012.net/〉. 〈10.1007/978-3-642-37382-4_8〉
Liste complète des métadonnées

Littérature citée [12 références]  Voir  Masquer  Télécharger

https://hal-lirmm.ccsd.cnrs.fr/lirmm-00732661
Contributeur : Pascal Poncelet <>
Soumis le : dimanche 16 septembre 2012 - 02:45:55
Dernière modification le : samedi 21 avril 2018 - 01:25:39
Document(s) archivé(s) le : vendredi 16 décembre 2016 - 13:56:52

Fichier

NFMC2012.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Elias Egho, Dino Ienco, Nicolas Jay, Amedeo Napoli, Pascal Poncelet, et al.. Healtcare Trajectory Mining by Combining Multi-dimensional Component and Itemsets. NFMCP: New Frontiers in Mining Complex Patterns, Sep 2012, Bristol, United Kingdom. Springer, New Frontiers in Mining Complex Patterns, Workshop in conjunction with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2012), LNCS (7765), 2013, 〈http://www.ecmlpkdd2012.net/〉. 〈10.1007/978-3-642-37382-4_8〉. 〈lirmm-00732661〉

Partager

Métriques

Consultations de la notice

518

Téléchargements de fichiers

318