SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
First-order temporal pattern mining with regular expression constraints
Data & Knowledge Engineering
Efficient mining of understandable patterns from multivariate interval time series
Data Mining and Knowledge Discovery
Aligning temporal data by sentinel events: discovering patterns in electronic health records
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Migration motif: a spatial - temporal pattern mining approach for financial markets
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Detect and track latent factors with online nonnegative matrix factorization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
Temporal pattern discovery in longitudinal electronic patient records
Data Mining and Knowledge Discovery
Discovering convolutive speech phones using sparseness and non-negativity
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Video Segmentation via Temporal Pattern Classification
IEEE Transactions on Multimedia
An integrated framework for suicide risk prediction
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining diabetes complication and treatment patterns for clinical decision support
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Large collections of electronic clinical records today provide us with a vast source of information on medical practice. However, the utilization of those data for exploratory analysis to support clinical decisions is still limited. Extracting useful patterns from such data is particularly challenging because it is longitudinal, sparse and heterogeneous. In this paper, we propose a Nonnegative Matrix Factorization (NMF) based framework using a convolutional approach for open-ended temporal pattern discovery over large collections of clinical records. We call the method One-Sided Convolutional NMF (OSC-NMF). Our framework can mine common as well as individual shift-invariant temporal patterns from heterogeneous events over different patient groups, and handle sparsity as well as scalability problems well. Furthermore, we use an event matrix based representation that can encode quantitatively all key temporal concepts including order, concurrency and synchronicity. We derive efficient multiplicative update rules for OSC-NMF, and also prove theoretically its convergence. Finally, the experimental results on both synthetic and real world electronic patient data are presented to demonstrate the effectiveness of the proposed method.