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|a eng
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| 100 |
1 |
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|a Wang, Fei
|e verfasserin
|4 aut
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| 245 |
1 |
2 |
|a A framework for mining signatures from event sequences and its applications in healthcare data
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| 264 |
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|c 2013
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| 336 |
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 13.08.2013
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| 500 |
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|a Date Revised 01.04.2013
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|a published: Print
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| 500 |
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|a Citation Status MEDLINE
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| 520 |
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|a 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
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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Lee, Noah
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hu, Jianying
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sun, Jimeng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ebadollahi, Shahram
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Laine, Andrew F
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 35(2013), 2 vom: 15. Feb., Seite 272-85
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|x 1939-3539
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|g pages:272-85
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