×

Using formal concept analysis for mining and interpreting patient flows within a healthcare network. (English) Zbl 1133.68452

Ben Yahia, Sadok (ed.) et al., Concept lattices and their applications. Fourth international conference, CLA 2006, Tunis, Tunisia, October 30–November 1, 2006. Selected papers. Berlin: Springer (ISBN 978-3-540-78920-8/pbk). Lecture Notes in Computer Science 4923. Lecture Notes in Artificial Intelligence, 263-268 (2008).
Summary: This paper presents an original experiment based on frequent itemset search and lattice based classification. This work focuses on the ability of iceberg-lattices to discover and represent flows of patient within a healthcare network. We give examples of analysis of real medical data showing how Formal Concept Analysis techniques can be helpful in the interpretation step of the knowledge discovery in databases process. This combined approach has been successfully used to assist public health managers in designing healthcare networks and planning medical resources.
For the entire collection see [Zbl 1132.68004].

MSC:

68T30 Knowledge representation
68T05 Learning and adaptive systems in artificial intelligence

Software:

Galicia
PDFBibTeX XMLCite
Full Text: DOI Link

References:

[1] Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P., The KDD Process for Extracting Useful Knowledge from Volumes of Data, Communication of the ACM, 29-11, 27-34 (1996) · doi:10.1145/240455.240464
[2] Ganter, B.; Wille, R., Formal Concept Analysis: mathematical foundations (1999), Heidelberg: Springer, Heidelberg · Zbl 0909.06001
[3] Jay, N., Napoli, A., Kohler, F.: Cancer Patient Flows Discovery in DRG Databases. In: Proc. MIE 2006 Conf. (to appear)
[4] Becker, R.A., Eick, S.G., Wilks, A.R.: Visualizing Network Data IEEE Transactions on Visualization and Computer Graphics, vol. 1, pp. 16-28 (1995)
[5] Freeman, L. C.; White, D. R., Using Galois Lattices to Represent Network Data, Sociological Methodology, 23, 127-146 (1993) · doi:10.2307/271008
[6] Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. of the ACM SIGMOD Conference on Management of Data, Washington, D.C, pp. 207-216 (May 1993)
[7] Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Closed set based discovery of small covers for association rules. In: Proc. BDA conf., pp. 361-381 (1999)
[8] Stumme, G.: Conceptual Knowledge Discovery with Frequent Concept Lattices. FB4-Preprint 2043, TU Darmstadt (1999)
[9] Zaki, M.J., Hsiao, C.: CHARM: An Efficient Algorithm for Closed Itemset Mining. In: SDM (2002)
[10] Stumme, G.; Taouil, R.; Bastide, Y.; Pasquier, N.; Lakhal, L., Computing iceberg concept lattices with TITANIC Data Knowl. Eng., 189-222 (2002), Amsterdam: Elsevier Science Publishers B. V, Amsterdam · Zbl 0996.68046
[11] Duquenne, V., Lattice analysis and the representation of handicap associations, Social Networks, 18, 217-230 (1996) · doi:10.1016/0378-8733(95)00274-X
[12] Duquenne, V., Latticial structures in data analysis, Theorical Computer Science, 217, 407-436 (1999) · Zbl 1034.68510 · doi:10.1016/S0304-3975(98)00279-5
[13] Valtchev, P.; Grosser, D.; Roume, C.; Hacene, M. R., GALICIA: An open platform for lattices, Using Conceptual Structures: Contributions to the 11th Intl. Conference on Conceptual Structures (ICCS 2003), 241-254 (2003), Ithaca: Shaker, Ithaca
[14] Gansner, E. R.; North, S. C., An open graph visualization system and its applications to software engineering, Softw. Pract. Exper., 30, 11, 1203-1233 (2000) · Zbl 1147.68782 · doi:10.1002/1097-024X(200009)30:11<1203::AID-SPE338>3.0.CO;2-N
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.