Application of machine learning in rheumatic disease research

• Main Authors: Ki-Jo Kim, Ilias Tagkopoulos
• Format: Article
• Language: English
• Published: The Korean Association of Internal Medicine 2019-07-01
• Series: The Korean Journal of Internal Medicine
• Subjects: rheumatology; machine learning; prediction
• Online Access: http://www.kjim.org/upload/pdf/kjim-2018-349.pdf

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.