Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization

By Joyce Ho, Joydeep Ghosh, Jimeng Sun. 2014

238 citations recorded by the source corpus.

Subfields: Computational Mathematics, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Federated Tensor Factorization for Computational Phenotyping (2017, methodology): “Overcoming several disadvantages of the previous methods, tensor factorization methods have shown great potential in discovering meaningful phenotypes from complicated and heterogeneous health records [13, 14, 28].”
Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “[143] applied sparse non-negative tensor factorization on counts of”
Tensor Factorization for Precision Medicine in Heart Failure with Preserved Ejection Fraction (2017, unknown): “[43] investigated the Centers for Medicare and Medicaid”
L G ] 1 1 A pr 2 01 7 Federated Tensor Factorization for Computational Phenotyping (2018, methodology): “Overcoming several disadvantages of the previous methods, tensor factorization methods have shown great potential in discovering meaningful phenotypes from complicated and heterogeneous health records [13, 14, 28].”
Predicting Complications in Critical Care Using Heterogeneous Clinical Data (2016, background): “[26] study phenotypes based on chronic disease indicators and procedure and diagnosis codes.”
SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping (2018, methodology): “They have been applied successfully in diverse applications, including social network analysis [31], text mining [14], image processing [35], recommendation systems [26], brain data analysis [2] and healthcare analytics [22], to name a few.”
Deep Computational Phenotyping (2015, background): “This field of research, known as computational phenotyping, has attracted many machine learning and data mining researchers, applying a broad set of methods to learn meaningful representations from a variety of data sources and types [39, 42, 17].”
FLASH: Fast Bayesian Optimization for Data Analytic Pipelines (2016, background): “tensor factorization [19] can be expensive to compute.”
Tensors for Data Mining and Data Fusion (2016, methodology): “In particular, the authors propose a sparse version of H-Tucker (which is presented in more detail in Section 5.3) and apply it to a disease phenotyping problem, much like the one addressed by Ho et al. [2014].”
Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery (2015, methodology): “More recently, Ho et al. have used tensor factorization as an alternative approach to summarizing high-dimensional vectors created from EHRs (Ho, Ghosh, and Sun 2014).”
Risk Prediction with Electronic Health Records: A Deep Learning Approach (2016, methodology): “[10, 16] proposed a nonnegative tensor factorization based approach for phenotype extraction from those EHR tensors.”
COPA: Constrained PARAFAC2 for Sparse & Large Datasets (2018, unknown): “sor factorization encompasses a set of powerful analytic methods that been successfully applied in many application domains: social network analysis, urban planning, text mining, and health analytics [2, 13, 14, 17, 18, 25]. However, modeling the time mode is intrinsically difficult for tensor facto”
Large-Scale Discovery of Disease-Disease and Disease-Gene Associations (2016, background): “The identification process of useful, meaningful medical characteristics and insights for the purposes of medical treatment is referred to as phenotyping 14 .”