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Machine Learning and Graph Signal Processing Applied to Healthcare: A Review.

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This review explores machine learning applied to graph signal processing in health sciences. The emerging field shows promise but requires improved clinical interpretability and new datasets.

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Area of Science:

  • Signal processing
  • Graph theory
  • Machine learning
  • Health sciences

Background:

  • Signal processing is crucial for interpreting everyday signals.
  • Graph theory extends signal processing to non-Euclidean domains for time-varying signals.
  • Machine learning is widely applied in pattern recognition, including health sciences.

Purpose of the Study:

  • To identify and analyze literature on machine learning applied to graph signal processing in health sciences.
  • To understand the current state and identify research gaps in this emerging area.

Main Methods:

  • A systematic literature search was conducted across four major databases: Science Direct, IEEE Xplore, ACM, and MDPI.
  • Specific search strings were used to identify relevant papers.
  • A total of 45 papers published from 2015 onwards were included in the analysis.

Main Results:

  • The application of machine learning to graph signal processing in health sciences is an emerging research area, with the first publications appearing in 2015.
  • Analysis revealed a need for enhanced clinical interpretability of results beyond mere performance metrics.
  • Identified research gaps include the exploration of novel transforms and the creation of new public datasets.

Conclusions:

  • Machine learning applied to graph signal processing is a growing field within health sciences.
  • Future research should focus on improving clinical relevance and developing new methodologies and accessible datasets.
  • Addressing these gaps will further advance the practical application of these techniques in healthcare.