Using linear regression functions to abstract high-frequency data in medicine.
Conference Proceeding Article
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.
Algorithm, Article, Intensive care, Intensive care unit, Statistical model, Statistics, Time
Computer Sciences | Health Information Technology
Data Management and Analytics
AMIA (American Medical Informatics Association) 2000 Annual Symposium
SYMPOSIUM SUPPLEMENT 12000
City or Country
Los Angeles, USA
Li, J. and Tze-Yun LEONG.
Using linear regression functions to abstract high-frequency data in medicine.. (2000). AMIA (American Medical Informatics Association) 2000 Annual Symposium. SYMPOSIUM SUPPLEMENT 12000, 492-496. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3053
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