Výsledky vyhledávání - "Perros, Ioakeim"

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TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records

Autor: Afshar, Ardavan, Perros, Ioakeim, Park, Haesun, deFilippi, Christopher, Yan, Xiaowei, Stewart, Walter, Ho, Joyce, Sun, Jimeng

Phenotyping electronic health records (EHR) focuses on defining meaningful patient groups (e.g., heart failure group and diabetes group) and identifying the temporal evolution of patients in those groups. Tensor factorization has been an effective to

Externí odkaz: http://arxiv.org/abs/1911.05843

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SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping

Autor: Perros, Ioakeim, Papalexakis, Evangelos E., Park, Haesun, Vuduc, Richard, Yan, Xiaowei, Defilippi, Christopher, Stewart, Walter F., Sun, Jimeng

This paper presents a new method, which we call SUSTain, that extends real-valued matrix and tensor factorizations to data where values are integers. Such data are common when the values correspond to event counts or ordinal measures. The conventiona

Externí odkaz: http://arxiv.org/abs/1803.05473

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COPA: Constrained PARAFAC2 for Sparse & Large Datasets

Autor: Afshar, Ardavan, Perros, Ioakeim, Papalexakis, Evangelos E., Searles, Elizabeth, Ho, Joyce, Sun, Jimeng

PARAFAC2 has demonstrated success in modeling irregular tensors, where the tensor dimensions vary across one of the modes. An example scenario is modeling treatments across a set of patients with the varying number of medical encounters over time. De

Externí odkaz: http://arxiv.org/abs/1803.04572

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SPARTan: Scalable PARAFAC2 for Large & Sparse Data

Autor: Perros, Ioakeim, Papalexakis, Evangelos E., Wang, Fei, Vuduc, Richard, Searles, Elizabeth, Thompson, Michael, Sun, Jimeng

In exploratory tensor mining, a common problem is how to analyze a set of variables across a set of subjects whose observations do not align naturally. For example, when modeling medical features across a set of patients, the number and duration of t

Externí odkaz: http://arxiv.org/abs/1703.04219

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Sparse Hierarchical Tucker Factorization and its Application to Healthcare

Autor: Perros, Ioakeim, Chen, Robert, Vuduc, Richard, Sun, Jimeng

We propose a new tensor factorization method, called the Sparse Hierarchical-Tucker (Sparse H-Tucker), for sparse and high-order data tensors. Sparse H-Tucker is inspired by its namesake, the classical Hierarchical Tucker method, which aims to comput

Externí odkaz: http://arxiv.org/abs/1610.07722

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Akademický článek

Using the PARAFAC2 tensor factorization on EHR audit data to understand PCP desktop work

Autor: Perros, Ioakeim, Yan, Xiaowei, Jones, J.B., Sun, Jimeng, Stewart, Walter F.

Publikováno v: In Journal of Biomedical Informatics January 2020 101

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Guaranteed Scalable Learning of Latent Tree Models

Autor: Huang, Furong, N., Niranjan U., Perros, Ioakeim, Chen, Robert, Sun, Jimeng, Anandkumar, Anima

We present an integrated approach for structure and parameter estimation in latent tree graphical models. Our overall approach follows a "divide-and-conquer" strategy that learns models over small groups of variables and iteratively merges onto a glo

Externí odkaz: http://arxiv.org/abs/1406.4566

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