Development of kernel deep stacking networks for improved medical diagnosis and prognosis

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Kernel deep stacking networks (KDSN) belong to the class of supervised deep learning methods, which are increasingly used for biomedical diagnosis and prognosis. Examples are the analysis of retinal images to predict disease progression in opththalmology, the prediction of splicing patterns in tissues, and the classification/prediction of neurological disorders.Compared to many other deep learning methods, KDSN are characterized by massively reduced run times, which is due to the fact that KDSN fitting is not based on the back-propagation algorithm but on a set of sequentially stacked and easy-to-solve kernel ridge regression problems. Due to the efficiency of KDSN, it is possible for biomedical researchers to apply deep-learning-based architectures without having to rely on the availability of sophisticated cloud- or GPU-based IT solutions. In previous work, we have implemented the KDSN method in R and have developed & published a data-driven procedure for KDSN tuning.The focus of this project is on several highly relevant extensions of KDSN, addressing issues that currently limit the widespread use of the method in biomedical applications. More specifically, the work packages of the project will include the development, implementation and analysis of (i) variable selection methods, (ii) extensions to time-to-event outcomes, (iii) techniques for dimension reduction, (iv) ensemble methods and (v) drop-out regularization in KDSN. In addition to simulation studies, all methodological developments will be tested with regard to their applicability in biomedical practice, including high-dimensional retinal image analysis and the analysis of longitudinal epidemiological study data.