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

作者：Batal Iyad^*; Cooper Gregory F; Fradkin Dmitriy; Harrison James Jr; Moerchen Fabian; Hauskrecht Milos

来源：Knowledge and Information Systems, 2016, 46(1): 115-150.

DOI：10.1007/s10115-015-0819-6

• Temporal data mining
• Electronic health records
• Temporal abstractions
• Time-interval patterns
• Recent temporal patterns
• Event detection

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.

• 出版日期2016-1