NDLI: Domain ontology-based feature reduction for high dimensional drug data and its application to 30-day heart failure readmission prediction

Content Provider	IEEE Xplore Digital Library
Author	Sisi Lu Ye Ye Tsui, R. Su, H. Rexit, R. Wesaratchakit, S. Xiaochu Liu Hwa, R.
Copyright Year	2013
Description	Author affiliation: Dept. of Biomed. Inf., Univ. of Pittsburgh, Pittsburgh, PA, USA (Sisi Lu; Ye Ye; Tsui, R.; Su, H.; Rexit, R.; Wesaratchakit, S.) \|\| Dept. of Comput. Sci., Univ. of Pittsburgh, Pittsburgh, PA, USA (Hwa, R.) \|\| Dept. of Comput. Sci. & Eng., Univ. of California, San Diego, La Jolla, CA, USA (Xiaochu Liu)
Abstract	High dimensional feature space could potentially hinder the efficiency and performance for machine learning, and high correlations between features may further increase the redundancy and diminish performance of learning algorithms. Domain ontology provides relationships and similarities between concepts in the specific area, and thus can be used to reduce redundancy by clustering concepts and revealing their functionality. In this paper, we study the problem of using high dimensional medication data to predict the probability of 30-Day heart failure readmission. We propose a feature reduction method for high dimensional dataset using a combination of two drug ontologies. By creating a tree structure of the combination, the method uses a greedy strategy to obtain a subset of features, which may have higher correlation with the class label but lower correlation with each other. Experimental results show that our methods improve the performance of heart failure readmission prediction (using only drug data) comparing to existing feature reduction methods without drug domain ontologies.
Starting Page	478
Ending Page	484
File Size	1299241
Page Count	7
File Format	PDF
ISBN	9781936968923
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	ICST
Subject Keyword	Drugs Heart Heart Failure Readmission Prediction Feature Reduction Feature Selection Machine learning algorithms Hospitals Ontologies Information filters Domain Ontology High Dimensional Data
Content Type	Text
Resource Type	Article

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