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.
|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|
|Number of pages||7|
|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
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||6th International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2015|
|Period||15/9/3 → 15/9/4|
Bibliographical noteFunding 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).
© Springer International Publishing Switzerland 2015.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)