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In this paper we propose a kernel-based framework for medical big data analytics to address the issue of heterogeneous data, which employs a neutral point substitution method to address the missing data problem presented by patients with sparse or absent data modalities

A Kernel-Based Framework for Medical Big-Data Analytics.

Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, pp.197-208, (2014)

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摘要

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 highdimensional, incomplete and inaccurate. Of these, standard pattern recogniti...更多

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简介
  • The introduction of electronic health records as a means of standardizing the recording and storage of healthcare information has been significantly accelerating in recent years, resulting in massive volumes of patient data being stored online in a manner readily accessible to clinicians and health-care professionals [1, 2].
  • In this paper the authors propose a kernel-based framework for medical big data analytics to address the issue of heterogeneous data, which employs a neutral point substitution method to address the missing data problem presented by patients with sparse or absent data modalities.
  • Since medical records contain many images (X-ray, MRI, etc) the authors propose to employ a deep-learning approach to address the problem of progressive areal segmentation, in order to improve analysis and classification of key organs or regions.
重点内容
  • The introduction of electronic health records as a means of standardizing the recording and storage of healthcare information has been significantly accelerating in recent years, resulting in massive volumes of patient data being stored online in a manner readily accessible to clinicians and health-care professionals [1, 2]
  • In this paper we propose a kernel-based framework for medical big data analytics to address the issue of heterogeneous data, which employs a neutral point substitution method to address the missing data problem presented by patients with sparse or absent data modalities
  • In summary, propose the hierarchical segmentation of medical data by using a deep learning approach. This will require experimentation with the methodology of convolutional deep belief networks in order to optimize the approach for hierarchical image decomposition in a manner most useful to medical objectives
  • In this paper we have outlined two novel research directions to address critical issues in big medical data analytics in a complementary manner: (1) a kernelbased framework for combining heterogeneous data with missing or uncertain elements and (2) a new approach to medical image segmentation built around hierarchical abstraction for later kernel-based characterization
  • The two approaches of kernel-based combination of heterogeneous data with neutral point substitution and deep-learning would we argue, address the major outstanding big-data challenges associated with Electronic Health Record standardization, in particular incompleteness and heterogeneity
结果
  • In general, make the problem of combining heterogeneous medical data much more tractable than would be the case for standard pattern recognition methods, there are certain caveats that are specific to them.
  • Segmentation would be employed, in particular, to identify individual organs prior to kernelized shape characterization for incorporation into the above medical data combination framework.
  • The authors propose to leverage the hierarchical decompositional characteristics of deep belief networks to address the problem of medical imaging, the aspects of progressive areal segmentation, in order to improve classification of key organs.
  • This will require experimentation with the methodology of convolutional deep belief networks in order to optimize the approach for hierarchical image decomposition in a manner most useful to medical objectives.
  • In this paper the authors have outlined two novel research directions to address critical issues in big medical data analytics in a complementary manner: (1) a kernelbased framework for combining heterogeneous data with missing or uncertain elements and (2) a new approach to medical image segmentation built around hierarchical abstraction for later kernel-based characterization.
  • Kernelization is the key to addressing data-heterogeneity issues; the way in which missing ‘intermodal’ data is combined in within the kernel-based framework depends on the authors’ neutral point substitution method.
  • The two approaches of kernel-based combination of heterogeneous data with neutral point substitution and deep-learning would the authors argue, address the major outstanding big-data challenges associated with Electronic Health Record standardization, in particular incompleteness and heterogeneity.
结论
  • Unsupervised clustering, in particular the problem of determining the presence of sub-populations within the data, might be addressed by addressed by model-fitting (Kernel regression [27], or perhaps K-means or Expectation Maximization with Gaussian Mixture Modelling in sufficiently low-dimensional vector spaces) in conjunction with an appropriate model-selection criterion.
  • Addressing the third open problem, Longitudinal Analysis, within the proposed kernel-based framework would likely involve the aforementioned Gaussian processes given their ready incorporation within the kernel methodology.
  • Appropriate modality weightings can be learned in multimodal problems of arbitrary data completion; the authors have the ability to combine any multiple modality data irrespective of modal omission
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