Ridge penalized logistical and ordinal partial least squares regression for predicting stroke deficit from infarct topography
Jian Chen, Thanh G. Phan, David C. Reutens
DOI: 10.4236/jbise.2010.36079   PDF    HTML     5,618 Downloads   10,064 Views   Citations


Improving the ability to assess potential stroke deficit may aid the selection of patients most likely to benefit from acute stroke therapies. Methods based only on ‘at risk’ volumes or initial neurological condition do predict eventual outcome but not perfectly. Given the close relationship between anatomy and function in the brain, we propose the use of a modified version of partial least squares (PLS) regression to examine how well stroke outcome covary with infarct location. The modified version of PLS incorporates penalized regression and can handle either binary or ordinal data. This version is known as partial least squares with penalized logistic regression (PLS-PLR) and has been adapted from its original use for high-dimensional microarray data. We have adapted this algorithm for use in imaging data and demonstrate the use of this algorithm in a set of patients with aphasia (high level language disorder) following stroke.

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Chen, J. , Phan, T. and Reutens, D. (2010) Ridge penalized logistical and ordinal partial least squares regression for predicting stroke deficit from infarct topography. Journal of Biomedical Science and Engineering, 3, 568-575. doi: 10.4236/jbise.2010.36079.

Conflicts of Interest

The authors declare no conflicts of interest.


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