An efficient pattern mining approach for event detection in multivariate temporal data

Batal I, Cooper G, Fradkin D, Harrison J, Moerchen F, Hauskrecht M. An efficient pattern mining approach for event detection in multivariate temporal data. The Journal of Knowledge and Information Systems (2015) 1-36 [PubMed – in progress].

This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present Recent Temporal Pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs more complex time-interval patterns backward in time using temporal operators. We also present the Minimal Predictive Recent Temporal Patterns framework for selecting a small set of predictive and non-spurious patterns. We apply our methods for predicting adverse medical events in real-world clinical data. The results demonstrate the benefits of our methods in learning accurate event detection models, which is a key step for developing intelligent patient monitoring and decision support systems.

Publication Year: 
2015
Publication Credits: 
Batal I, Cooper G, Fradkin D, Harrison J, Moerchen F, Hauskrecht M.
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