Name
Title | ||
|---|---|---|
A framework for mining signatures from event sequences and its applications in healthcare data. |
Abstract | ||
|---|---|---|
This paper proposes a novel temporal knowledge representation and learning framework to perform large-scale temporal signature mining of longitudinal heterogeneous event data. The framework enables the representation, extraction, and mining of high-order latent event structure and relationships within single and multiple event sequences. The proposed knowledge representation maps the heterogeneous event sequences to a geometric image by encoding events as a structured spatial-temporal shape process. We present a doubly constrained convolutional sparse coding framework that learns interpretable and shift-invariant latent temporal event signatures. We show how to cope with the sparsity in the data as well as in the latent factor model by inducing a double sparsity constraint on the β-divergence to learn an overcomplete sparse latent factor model. A novel stochastic optimization scheme performs large-scale incremental learning of group-specific temporal event signatures. We validate the framework on synthetic data and on an electronic health record dataset. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TPAMI.2012.111 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
heterogeneous event sequence,event sequences,novel temporal knowledge representation,mining signatures,healthcare data,latent factor model,longitudinal heterogeneous event data,group-specific temporal event signature,encoding event,high-order latent event structure,temporal event signature,multiple event sequence,large-scale temporal signature mining,sparse coding,stochastic gradient descent,health care,stochastic programming,convolution,synthetic data,data mining,nonnegative matrix factorization,learning artificial intelligence,convergence,sparse matrices,knowledge representation | Data mining,Knowledge representation and reasoning,Stochastic gradient descent,Pattern recognition,Computer science,Neural coding,Synthetic data,Artificial intelligence,Non-negative matrix factorization,Event structure,Sparse matrix,Encoding (memory) | Journal |
Volume | Issue | ISSN |
35 | 2 | 1939-3539 |
Citations | PageRank | References |
21 | 0.89 | 12 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Wang | 1 | 21 | 0.89 |
| Noah Lee | 2 | 128 | 7.70 |
| Jianying Hu | 3 | 478 | 35.52 |
| Jimeng Sun | 4 | 4729 | 240.91 |
| Shahram Ebadollahi | 5 | 275 | 23.21 |
| Andrew F. Laine | 6 | 747 | 83.01 |