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.
authors.affiliation: IBM T.J. Watson Research Center, 19 Skyline Dr., Hawthorne, NY 10532, USA. fwang@us.ibm.com
authors.forename: Fei
authors.initials: F
authors.lastname: Wang
authors.forename: Noah
authors.initials: N
authors.lastname: Lee
authors.forename: Jianying
authors.initials: J
authors.lastname: Hu
authors.forename: Jimeng
authors.initials: J
authors.lastname: Sun
authors.forename: Shahram
authors.initials: S
authors.lastname: Ebadollahi
authors.forename: Andrew F
authors.initials: AF
authors.lastname: Laine
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D003628:Database Management Systems; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D057286:Electronic Health Records; D055991:Health Records, Personal; D010363:Pattern Recognition, Automated / Q000379:methods*