Mining recent temporal patterns for event detection in multivariate time series data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Multivariate time series classification by combining trend-based and value-based approximations
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part IV
Learning pattern graphs for multivariate temporal pattern retrieval
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Proceedings of the Winter Simulation Conference
Learning classification models from multiple experts
Journal of Biomedical Informatics
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We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task. To address this problem, we present the minimal predictive temporal patterns framework to generate a small set of predictive and non-spurious patterns. We apply our approach to the real-world clinical task of predicting patients who are at risk of developing heparin induced thrombocytopenia. The results demonstrate the benefit of our approach in learning accurate classifiers, which is a key step for developing intelligent clinical monitoring systems.