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Application of machine learning in rheumatic disease research

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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.

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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