Article

Deep learning on electronic medical records identifies distinct subphenotypes of diabetic kidney disease driven by genetic variations in the<i>Rho</i>pathway

2023-09-07

Abstract excerpt

<h4>A bstract </h4> Kidney disease affects 50% of all diabetic patients; however, prediction of disease progression has been challenging due to inherent disease heterogeneity. We use deep learning to identify novel genetic signatures prognostically associated with outcomes. Using autoencoders and unsupervised clustering of electronic health record data on 1,372 diabetic kidney disease patients, we establish two cl...

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Literature Corpus work: 0e632ce0-0cdf-5318-bdaf-4e64df161dac
DOI: 10.1101/2023.09.06.23295120

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Deep learning on electronic medical records identifies distinct subphenotypes of diabetic kidney disease driven by genetic variations in the<i>Rho</i>pathwayDOI 10.1101/2023.09.06.23295120Read

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