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Machine Learning and Mobile Health Monitoring Platforms: A Case Study on Research and Implementation Challenges

Abstract

Machine learning-based patient monitoring systems are generally deployed on remote servers for analyzing heterogeneous data. While recent advances in mobile technology provide new opportunities to deploy such systems directly on mobile devices, the development and deployment challenges are not being extensively studied by the research community. In this paper, we systematically investigate challenges associated with each stage of the development and deployment of a machine learning-based patient monitoring system on a mobile device. For each class of challenges, we provide a number of recommendations that can be used by the researchers, system designers, and developers working on mobile-based predictive and monitoring systems. The results of our investigation show that when developers are dealing with mobile platforms, they must evaluate the predictive systems based on its classification and computational performance. Accordingly, we propose a new machine learning training and deployment methodology specifically tailored for mobile platforms that incorporates metrics beyond traditional classifier performance.

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Acknowledgments

Support from the McMaster School of Biomedical Engineering, McMaster Science & Research Board (SERB), Vector Institute for Artificial Intelligence, and Natural Sciences & Engineering Research Council of Canada (NSERC) is acknowledged.

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Correspondence to Omar Boursalie.

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Boursalie, O., Samavi, R. & Doyle, T.E. Machine Learning and Mobile Health Monitoring Platforms: A Case Study on Research and Implementation Challenges. J Healthc Inform Res 2, 179–203 (2018). https://doi.org/10.1007/s41666-018-0021-1

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  • DOI: https://doi.org/10.1007/s41666-018-0021-1

Keywords

  • Remote patient monitoring
  • Wearable system
  • SVM
  • MLP
  • Data mining
  • Mobile device
  • Health records
  • Severity estimation
  • Data fusion
  • Machine learning
  • System development