title: An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data.
abstract: 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.
authors.affiliation: GE Global Research, iyad.batal@ge.com.
authors.forename: Iyad
authors.initials: I
authors.lastname: Batal
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, gfc@pitt.edu.
authors.forename: Gregory
authors.initials: G
authors.lastname: Cooper
authors.affiliation: Siemens Corporate Research, dmitriy.fradkin@siemens.com.
authors.forename: Dmitriy
authors.initials: D
authors.lastname: Fradkin
authors.affiliation: Department of Public Health Sciences, University of Virginia, james.harrison@virginia.edu.
authors.forename: James
authors.initials: J
authors.lastname: Harrison
authors.affiliation: Amazon, moerchen@amazon.com.
authors.forename: Fabian
authors.initials: F
authors.lastname: Moerchen
authors.affiliation: Department of Computer Science, University of Pittsburgh, milos@cs.pitt.edu.
authors.forename: Milos
authors.initials: M
authors.lastname: Hauskrecht