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Application of machine learning in rheumatic disease research.

  • Author(s): Kim, Ki-Jo
  • Tagkopoulos, Ilias
  • et al.

Published Web Location

http://kjim.org/journal/view.php?doi=10.3904/kjim.2018.349
No data is associated with this publication.
Abstract

Over the past decade, there has been a paradigm shift in how clinical data are collected, processed and utilized. Machine learning and artificial intelligence, fueled by breakthroughs in high-performance computing, data availability and algorithmic innovations, are paving the way to effective analyses of large, multi-dimensional collections of patient histories, laboratory results, treatments, and outcomes. In the new era of machine learning and predictive analytics, the impact on clinical decision-making in all clinical areas, including rheumatology, will be unprecedented. Here we provide a critical review of the machine-learning methods currently used in the analysis of clinical data, the advantages and limitations of these methods, and how they can be leveraged within the field of rheumatology.

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