Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2014
DOI: 10.1145/2623330.2623755
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Clinical risk prediction with multilinear sparse logistic regression

Abstract: Logistic regression is one core predictive modeling technique that has been used extensively in health and biomedical problems. Recently a lot of research has been focusing on enforcing sparsity on the learned model to enhance its effectiveness and interpretability, which results in sparse logistic regression model. However, no matter the original or sparse logistic regression, they require the inputs to be in vector form. This limits the applicability of logistic regression in the problems when the data canno… Show more

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Cited by 40 publications
(32 citation statements)
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“…This technique is computationally expensive and would not work efficiently with a large dataset, therefore, they only focused on a small number of diagnoses. By taking advantage of a different set of inputs, functional magnetic resonance imaging (fMRI) images, Wang et al proposed high-order sparse logistic regression and multilinear sparse logistic regression [18,19] for early detection of Alzheimer disease and congestive heart failure. Their results surpassed standard learning algorithms, such as nearest neighbor, support vector machines (SVM), logistic regression (LR), and sparse logistic regression.…”
Section: Introductionmentioning
confidence: 99%
“…This technique is computationally expensive and would not work efficiently with a large dataset, therefore, they only focused on a small number of diagnoses. By taking advantage of a different set of inputs, functional magnetic resonance imaging (fMRI) images, Wang et al proposed high-order sparse logistic regression and multilinear sparse logistic regression [18,19] for early detection of Alzheimer disease and congestive heart failure. Their results surpassed standard learning algorithms, such as nearest neighbor, support vector machines (SVM), logistic regression (LR), and sparse logistic regression.…”
Section: Introductionmentioning
confidence: 99%
“…They build a patient-diagnosis-procedure tensor and apply CP-APR factorization to decompose it as summations of rank-1 bias tensors and rank-R interaction tensors with sparsity constraints on the factor matrices of interaction tensors, in order to explicitly account for interactions among groups of the same modality. Wang et al 44 studied the problem of predicting the onset risk of patients with heart failure. They applied tensor modeling to generalize sparse logistic regression to multiple modalities on EHR data, such as comorbidity diagnosis codes and medications, and called their model High Order Sparse Logistic Regression (HOSLR).…”
Section: Tensor Factorization: a Potential Solution For Multi-modal Dmentioning
confidence: 99%
“…For example, Wang et al [4] presented a multilinear sparse logistic regression for risk prediction with patient EHRs. Zhang et al [5] proposed a similarity based approach for personalized treatment recommendation.…”
Section: Introductionmentioning
confidence: 99%