Building ICU artifact detection models with more data in less time.
As many as 86% of intensive care unit (ICU) alarms are false. Multiple signal integration of temporal monitor data by decision tree induction may improve artifact detection. We explore the effect of data granularity on model-building by comparing models made from 1-second versus 1-minute data. Models developed from 1-minute data remained effective when tested on 1-second data. Model development using 1-minute data means that more hours of ICU monitoring (including more artifacts) can be processed in less time. Compression of temporal data by arithmetic mean, therefore, can be an effective method for decreasing knowledge discovery processing time without compromising learning.
Tsien
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CL
BWH/MGH Harvard Affiliated Emergency Medicine, Boston, MA, USA.
Kohane
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IS
McIntosh
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N
D016477:Artifacts; D003663:Decision Trees; D006801:Humans; D007362:Intensive Care Units; D008433:Mathematics; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D012372:ROC Curve; D013995:Time