Title

Using linear regression functions to abstract high-frequency data in medicine.

Publication Type

Conference Proceeding Article

Publication Date

9-2000

Abstract

This paper investigates the problem of representing medical time series in linear piece-wise functions and proposes a novel algorithm to transform time-stamped numeric data into simple linear regression functions. We apply methods that involve the hat matrix leverage value and the studentized deleted residual to identify outliers, and a heuristic approach to remove them from the data sets. By distinguishing the breaking points from true outliers, we can efficiently break the data set with respect to the underlying patterns. Using a rough segmentation step, our approach avoids using the whole data set as input, and reduces space requirement. The experimental results indicate our method can achieve more accurate representation of the underlying patterns in data sets collected in the intensive care units efficiently.

Keywords

Algorithm, Article, Intensive care, Intensive care unit, Statistical model, Statistics, Time

Discipline

Computer Sciences | Health Information Technology

Research Areas

Data Management and Analytics

Publication

AMIA (American Medical Informatics Association) 2000 Annual Symposium

Volume

SYMPOSIUM SUPPLEMENT 12000

First Page

492

Last Page

496

ISBN

156053480X

City or Country

Los Angeles, USA

Additional URL

http://api.elsevier.com/content/abstract/scopus_id/0034566950

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