The discovery of prognosis factors using association rule mining in acute myocardial infarction with ST-segment elevation

Kwang Sun Ryu; Hyun Woo Park; Soo Ho Park; Ibrahim M. Ishag; Jang Hwang Bae; Keun Ho Ryu

doi:10.1007/978-3-319-22741-2_5

The discovery of prognosis factors using association rule mining in acute myocardial infarction with ST-segment elevation

Kwang Sun Ryu, Hyun Woo Park, Soo Ho Park, Ibrahim M. Ishag, Jang Hwang Bae, Keun Ho Ryu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Association rule mining has been applied actively in order to discover the hidden factors in acute myocardial infarction. There has been minimal research regarding the prognosis factor of acute myocardial infarction, and several previous studies has some limitations which are generation of incorrect population and potential data bias. Thus, we suggest the generation of prognosis factor based on association rule mining for acute myocardial infarction with ST-segment elevation. In our experiments, we obtain high interestingness factor based on Korean acute myocardial infarction registry which is corrected by 51 participating hospitals since 2005. The interestingness of the factor is evaluated by confidence. It is expected to contribute to prognosis management by high reliability factor.

Original language	English
Title of host publication	Information Technology in Bio- and Medical Informatics - 6th International Conference, ITBAM 2015, Proceedings
Editors	M. Elena Renda, Andreas Holzinger, Sami Khuri, Miroslav Bursa
Publisher	Springer Verlag
Pages	49-55
Number of pages	7
ISBN (Print)	9783319227405
DOIs	https://doi.org/10.1007/978-3-319-22741-2_5
Publication status	Published - 2015
Event	6th International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2015 - Valencia, Spain Duration: 2015 Sept 3 → 2015 Sept 4

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9267
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2015
Country/Territory	Spain
City	Valencia
Period	15/9/3 → 15/9/4

Bibliographical note

Funding Information:
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No.2013R1A2A2A01068923) and by the ITRC(Information Technology Research Center) support program (NIPA-2014-H0301-14-1002).

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-22741-2_5

Cite this

Sun Ryu, K., Woo Park, H., Ho Park, S., Ishag, I. M., Hwang Bae, J., & Ho Ryu, K. (2015). The discovery of prognosis factors using association rule mining in acute myocardial infarction with ST-segment elevation. In M. Elena Renda, A. Holzinger, S. Khuri, & M. Bursa (Eds.), Information Technology in Bio- and Medical Informatics - 6th International Conference, ITBAM 2015, Proceedings (pp. 49-55). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9267). Springer Verlag. https://doi.org/10.1007/978-3-319-22741-2_5

Sun Ryu, Kwang ; Woo Park, Hyun ; Ho Park, Soo et al. / The discovery of prognosis factors using association rule mining in acute myocardial infarction with ST-segment elevation. Information Technology in Bio- and Medical Informatics - 6th International Conference, ITBAM 2015, Proceedings. editor / M. Elena Renda ; Andreas Holzinger ; Sami Khuri ; Miroslav Bursa. Springer Verlag, 2015. pp. 49-55 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{c9b7ea5c7dff47809614f95b6cae8204,

title = "The discovery of prognosis factors using association rule mining in acute myocardial infarction with ST-segment elevation",

abstract = "Association rule mining has been applied actively in order to discover the hidden factors in acute myocardial infarction. There has been minimal research regarding the prognosis factor of acute myocardial infarction, and several previous studies has some limitations which are generation of incorrect population and potential data bias. Thus, we suggest the generation of prognosis factor based on association rule mining for acute myocardial infarction with ST-segment elevation. In our experiments, we obtain high interestingness factor based on Korean acute myocardial infarction registry which is corrected by 51 participating hospitals since 2005. The interestingness of the factor is evaluated by confidence. It is expected to contribute to prognosis management by high reliability factor.",

author = "{Sun Ryu}, Kwang and {Woo Park}, Hyun and {Ho Park}, Soo and Ishag, {Ibrahim M.} and {Hwang Bae}, Jang and {Ho Ryu}, Keun",

note = "Funding Information: This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No.2013R1A2A2A01068923) and by the ITRC(Information Technology Research Center) support program (NIPA-2014-H0301-14-1002). Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 6th International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2015 ; Conference date: 03-09-2015 Through 04-09-2015",

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Sun Ryu, K, Woo Park, H, Ho Park, S, Ishag, IM, Hwang Bae, J & Ho Ryu, K 2015, The discovery of prognosis factors using association rule mining in acute myocardial infarction with ST-segment elevation. in M Elena Renda, A Holzinger, S Khuri & M Bursa (eds), Information Technology in Bio- and Medical Informatics - 6th International Conference, ITBAM 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9267, Springer Verlag, pp. 49-55, 6th International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2015, Valencia, Spain, 15/9/3. https://doi.org/10.1007/978-3-319-22741-2_5

The discovery of prognosis factors using association rule mining in acute myocardial infarction with ST-segment elevation. / Sun Ryu, Kwang; Woo Park, Hyun; Ho Park, Soo et al.

Information Technology in Bio- and Medical Informatics - 6th International Conference, ITBAM 2015, Proceedings. ed. / M. Elena Renda; Andreas Holzinger; Sami Khuri; Miroslav Bursa. Springer Verlag, 2015. p. 49-55 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9267).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Woo Park, Hyun

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AU - Ishag, Ibrahim M.

AU - Hwang Bae, Jang

AU - Ho Ryu, Keun

N1 - Funding Information: This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No.2013R1A2A2A01068923) and by the ITRC(Information Technology Research Center) support program (NIPA-2014-H0301-14-1002). Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

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N2 - Association rule mining has been applied actively in order to discover the hidden factors in acute myocardial infarction. There has been minimal research regarding the prognosis factor of acute myocardial infarction, and several previous studies has some limitations which are generation of incorrect population and potential data bias. Thus, we suggest the generation of prognosis factor based on association rule mining for acute myocardial infarction with ST-segment elevation. In our experiments, we obtain high interestingness factor based on Korean acute myocardial infarction registry which is corrected by 51 participating hospitals since 2005. The interestingness of the factor is evaluated by confidence. It is expected to contribute to prognosis management by high reliability factor.

AB - Association rule mining has been applied actively in order to discover the hidden factors in acute myocardial infarction. There has been minimal research regarding the prognosis factor of acute myocardial infarction, and several previous studies has some limitations which are generation of incorrect population and potential data bias. Thus, we suggest the generation of prognosis factor based on association rule mining for acute myocardial infarction with ST-segment elevation. In our experiments, we obtain high interestingness factor based on Korean acute myocardial infarction registry which is corrected by 51 participating hospitals since 2005. The interestingness of the factor is evaluated by confidence. It is expected to contribute to prognosis management by high reliability factor.

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

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ER -

Sun Ryu K, Woo Park H, Ho Park S, Ishag IM, Hwang Bae J, Ho Ryu K. The discovery of prognosis factors using association rule mining in acute myocardial infarction with ST-segment elevation. In Elena Renda M, Holzinger A, Khuri S, Bursa M, editors, Information Technology in Bio- and Medical Informatics - 6th International Conference, ITBAM 2015, Proceedings. Springer Verlag. 2015. p. 49-55. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-22741-2_5

The discovery of prognosis factors using association rule mining in acute myocardial infarction with ST-segment elevation

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