A kernel-based framework for medical big-data analytics

Book chapter

Windridge, D. and Bober, M. 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 Springer. pp. 197-208
Chapter titleA kernel-based framework for medical big-data analytics
AuthorsWindridge, D. and Bober, M.
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.

Page range197-208
Book titleInteractive Knowledge Discovery and Data Mining in Biomedical Informatics: State-of-the-Art and Future Challenges
PublisherSpringer
SeriesLecture Notes in Computer Science
ISBN
Paperback9783662439678
Electronic9783662439685
ISSN0302-9743
Electronic1611-3349
Publication dates
Online17 Jun 2014
Print26 Jun 2014
Publication process dates
Deposited22 Apr 2016
Accepted07 May 2014
Output statusPublished
Accepted author manuscript
Copyright Statement

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-662-43968-5_11

Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-662-43968-5_11
Scopus EID2-s2.0-84905272228
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Looking to score: the dissociation of goal influence on eye movement and meta-attentional allocation in a complex dynamic natural scene
Taya, S., Windridge, D. and Osman, M. 2012. Looking to score: the dissociation of goal influence on eye movement and meta-attentional allocation in a complex dynamic natural scene. PLoS ONE. 7 (6). https://doi.org/10.1371/journal.pone.0039060
Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution
Poh, N., Windridge, D., Mottl, V., Tatarchuk, A. and Eliseyev, A. 2010. Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution. IEEE Transactions on Information Forensics and Security. 5 (3), pp. 461-469. https://doi.org/10.1109/TIFS.2010.2053535