Contrast set mining for distinguishing between similar diseases

Petra Kralj; Nada Lavrač; Dragan Gamberger; Antonija Krstačić

doi:10.1007/978-3-540-73599-1_12

Contrast set mining for distinguishing between similar diseases

Petra Kralj^*
, Nada Lavrač
, Dragan Gamberger
, Antonija Krstačić

^*Corresponding author for this work

Jožef Stefan Institute
University of Nova Gorica
Ruder Boskovic Institute
University of Zagreb

Research output: Contribution to Book/Report types › Conference contribution › peer-review

Abstract (may include machine translation)

The task addressed and the method proposed in this paper aim at improved understanding of differences between similar diseases. In particular we address the problem of distinguishing between thrombolic brain stroke and embolic brain stroke as an application of our approach of contrast set mining through subgroup discovery. We describe methodological lessons learned in the analysis of brain ischaemia data and a practical implementation of the approach within an open source data mining toolbox.

Original language	English
Title of host publication	Artificial Intelligence in Medicine - 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Proceedings
Publisher	Springer Verlag
Pages	109-118
Number of pages	10
ISBN (Print)	3540735984, 9783540735984
DOIs	https://doi.org/10.1007/978-3-540-73599-1_12
State	Published - 2007
Externally published	Yes
Event	11th Conference on Artificial Intelligence in Medicine, AIME 2007 - Amsterdam, Netherlands Duration: 7 Jul 2007 → 11 Jul 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4594 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th Conference on Artificial Intelligence in Medicine, AIME 2007
Country/Territory	Netherlands
City	Amsterdam
Period	7/07/07 → 11/07/07

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1007/978-3-540-73599-1_12

Cite this

Kralj, P., Lavrač, N., Gamberger, D., & Krstačić, A. (2007). Contrast set mining for distinguishing between similar diseases. In Artificial Intelligence in Medicine - 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Proceedings (pp. 109-118). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4594 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-73599-1_12

Kralj, Petra ; Lavrač, Nada ; Gamberger, Dragan et al. / Contrast set mining for distinguishing between similar diseases. Artificial Intelligence in Medicine - 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Proceedings. Springer Verlag, 2007. pp. 109-118 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{74dfc8a22de245b5bf9c70f4e90ab1c4,

title = "Contrast set mining for distinguishing between similar diseases",

abstract = "The task addressed and the method proposed in this paper aim at improved understanding of differences between similar diseases. In particular we address the problem of distinguishing between thrombolic brain stroke and embolic brain stroke as an application of our approach of contrast set mining through subgroup discovery. We describe methodological lessons learned in the analysis of brain ischaemia data and a practical implementation of the approach within an open source data mining toolbox.",

author = "Petra Kralj and Nada Lavra{\v c} and Dragan Gamberger and Antonija Krsta{\v c}i{\'c}",

year = "2007",

doi = "10.1007/978-3-540-73599-1\_12",

language = "English",

isbn = "3540735984",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "109--118",

booktitle = "Artificial Intelligence in Medicine - 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Proceedings",

note = "11th Conference on Artificial Intelligence in Medicine, AIME 2007 ; Conference date: 07-07-2007 Through 11-07-2007",

}

Kralj, P, Lavrač, N, Gamberger, D & Krstačić, A 2007, Contrast set mining for distinguishing between similar diseases. in Artificial Intelligence in Medicine - 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4594 LNAI, Springer Verlag, pp. 109-118, 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Amsterdam, Netherlands, 7/07/07. https://doi.org/10.1007/978-3-540-73599-1_12

Contrast set mining for distinguishing between similar diseases. / Kralj, Petra; Lavrač, Nada; Gamberger, Dragan et al.
Artificial Intelligence in Medicine - 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Proceedings. Springer Verlag, 2007. p. 109-118 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4594 LNAI).

Research output: Contribution to Book/Report types › Conference contribution › peer-review

TY - GEN

T1 - Contrast set mining for distinguishing between similar diseases

AU - Kralj, Petra

AU - Lavrač, Nada

AU - Gamberger, Dragan

AU - Krstačić, Antonija

PY - 2007

Y1 - 2007

N2 - The task addressed and the method proposed in this paper aim at improved understanding of differences between similar diseases. In particular we address the problem of distinguishing between thrombolic brain stroke and embolic brain stroke as an application of our approach of contrast set mining through subgroup discovery. We describe methodological lessons learned in the analysis of brain ischaemia data and a practical implementation of the approach within an open source data mining toolbox.

AB - The task addressed and the method proposed in this paper aim at improved understanding of differences between similar diseases. In particular we address the problem of distinguishing between thrombolic brain stroke and embolic brain stroke as an application of our approach of contrast set mining through subgroup discovery. We describe methodological lessons learned in the analysis of brain ischaemia data and a practical implementation of the approach within an open source data mining toolbox.

UR - https://www.scopus.com/pages/publications/35048890595

U2 - 10.1007/978-3-540-73599-1_12

DO - 10.1007/978-3-540-73599-1_12

M3 - Conference contribution

AN - SCOPUS:35048890595

SN - 3540735984

SN - 9783540735984

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 109

EP - 118

BT - Artificial Intelligence in Medicine - 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Proceedings

PB - Springer Verlag

T2 - 11th Conference on Artificial Intelligence in Medicine, AIME 2007

Y2 - 7 July 2007 through 11 July 2007

ER -

Kralj P, Lavrač N, Gamberger D, Krstačić A. Contrast set mining for distinguishing between similar diseases. In Artificial Intelligence in Medicine - 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Proceedings. Springer Verlag. 2007. p. 109-118. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-73599-1_12

Contrast set mining for distinguishing between similar diseases

Abstract (may include machine translation)

Publication series

Conference

UN SDGs

Access to Document

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Cite this