2014 ClinicalRiskPredictionwithMulti

• (Wang et al., 2014) ⇒ Fei Wang, Ping Zhang, Buyue Qian, Xiang Wang, and Ian Davidson. (2014). “Clinical Risk Prediction with Multilinear Sparse Logistic Regression.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623755

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Cited By

• http://scholar.google.com/scholar?q=%222014%22+Clinical+Risk+Prediction+with+Multilinear+Sparse+Logistic+Regression
• http://dl.acm.org/citation.cfm?id=2623330.2623755&preflayout=flat#citedby

Quotes

Author Keywords

• Correlation and regression analysis; health; healthcare; logistic regression; multilinear; proximal gradient

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 cannot be naturally represented vectors (e.g., functional magnetic resonance imaging and electroencephalography signals). To handle the cases when the data are in the form of multi-dimensional arrays, we propose MulSLR: Multilinear Sparse Logistic Regression. MulSLR can be viewed as a high order extension of sparse logistic regression. Instead of solving one classification vector as in conventional logistic regression, we solve for K classification vectors in MulSLR (K is the number of modes in the data). We propose a block proximal descent approach to solve the problem and prove its convergence. The convergence rate of the proposed algorithm is also analyzed. Finally we validate the efficiency and effectiveness of MulSLR on predicting the onset risk of patients with Alzheimer's disease and heart failure.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 ClinicalRiskPredictionwithMulti	Ian Davidson Xiang Wang Fei Wang Ping Zhang Buyue Qian			Clinical Risk Prediction with Multilinear Sparse Logistic Regression				10.1145/2623330.2623755		2014