A framework for mining signatures from event sequences and its applications in healthcare data

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fei WangAndrew F Laine

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.

References

Apr 17, 2009·The Journal of Neuroscience : the Official Journal of the Society for Neuroscience·Jonathon ShlensE J Chichilnisky
Nov 21, 2009·IEEE Transactions on Pattern Analysis and Machine Intelligence·Chris DingMichael I Jordan

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Citations

Jul 15, 2015·IEEE Journal of Biomedical and Health Informatics·Shameek GhoshJinyan Li
Jan 5, 2018·Statistical Methods in Medical Research·Luca Bonomi, Xiaoqian Jiang
Dec 12, 2018·Briefings in Bioinformatics·Chang SuFei Wang
Dec 29, 2020·Journal of Biomedical Informatics·Luca BonomiXiaoqian Jiang
Mar 13, 2021·Journal of Biomedical Informatics·Yuqi SiKirk Roberts

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