Towards a general theory of action and time
Artificial Intelligence
Neural networks: a systematic introduction
Neural networks: a systematic introduction
A framework for knowledge-based temporal abstraction
Artificial Intelligence
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Knowledge Discovery from Series of Interval Events
Journal of Intelligent Information Systems - Data warehousing and knowledge discovery
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Adaptive Intrusion Detection: A Data Mining Approach
Artificial Intelligence Review - Issues on the application of data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovering Temporal Patterns for Interval-Based Events
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Mining hepatitis data with temporal abstraction
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive stream resource management using Kalman Filters
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Discovering Frequent Arrangements of Temporal Intervals
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Algorithms for time series knowledge mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Nonambiguous Temporal Patterns for Interval-Based Events
IEEE Transactions on Knowledge and Data Engineering
Data & Knowledge Engineering
Data mining with Temporal Abstractions: learning rules from time series
Data Mining and Knowledge Discovery
Data & Knowledge Engineering
Direct mining of discriminative and essential frequent patterns via model-based search tree
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Classification of multivariate time series using two-dimensional singular value decomposition
Knowledge-Based Systems
DynaMMo: mining and summarization of coevolving sequences with missing values
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A Supervised Time Series Feature Extraction Technique Using DCT and DWT
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Constructing classification features using minimal predictive patterns
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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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 nonspurious 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 efficiently learning accurate classifiers, which is a key step for developing intelligent clinical monitoring systems.