期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2015
卷号：5
期号：6
页码：13
DOI：10.5121/ijdkp.2015.5602
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Large amount of heterogeneous medical data is generated every day in various healthcare organizations.Those data could derive insights for improving monitoring and care delivery in the Intensive Care Unit.Conversely, these data presents a challenge in reducing this amount of data without information loss.Dimension reduction is considered the most popular approach for reducing data size and also to reducenoise and redundancies in data. In this paper, we are investigate the effect of the average laboratory testvalue and number of total laboratory in predicting patient deterioration in the Intensive Care Unit, wherewe consider laboratory tests as features. Choosing a subset of features would mean choosing the mostimportant lab tests to perform. Thus, our approach uses state-of-the-art feature selection to identify themost discriminative attributes, where we would have a better understanding of patient deteriorationproblem. If the number of tests can be reduced by identifying the most important tests, then we could alsoidentify the redundant tests. By omitting the redundant tests, observation time could be reduced and earlytreatment could be provided to avoid the risk. Additionally, unnecessary monetary cost would be avoided.We apply our technique on the publicly available MIMIC-II database and show the effectiveness of thefeature selection. We also provide a detailed analysis of the best features identified by our approach.
