A kernel-based framework for medical big-data analytics

Windridge, David ORCID: https://orcid.org/0000-0001-5507-8516 and Bober, Miroslaw (2014) A kernel-based framework for medical big-data analytics. In: Interactive Knowledge Discovery and Data Mining in Biomedical Informatics: State-of-the-Art and Future Challenges. Lecture Notes in Computer Science, 8401 . Springer, pp. 197-208. ISBN 9783662439678. [Book Section] (doi:10.1007/978-3-662-43968-5_11)

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Abstract

The recent trend towards standardization of Electronic Health Records (EHRs) represents a significant opportunity and challenge for medical big-data analytics. The challenge typically arises from the nature of the data which may be heterogeneous, sparse, very high-dimensional, incomplete and inaccurate. Of these, standard pattern recognition methods can typically address issues of high-dimensionality, sparsity and inaccuracy. The remaining issues of incompleteness and heterogeneity however are problematic; data can be as diverse as handwritten notes, blood-pressure readings and MR scans, and typically very little of this data will be co-present for each patient at any given time interval.
We therefore advocate a kernel-based framework as being most appropriate for handling these issues, using the neutral point substitution method to accommodate missing inter-modal data. For pre-processing of image-based MR data we advocate a Deep Learning solution for contextual areal segmentation, with edit-distance based kernel measurement then used to characterize relevant morphology.

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