Application of machine learning in rheumatic disease research
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 analys...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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The Korean Association of Internal Medicine
2019-07-01
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| Series: | The Korean Journal of Internal Medicine |
| Subjects: | |
| Online Access: | http://www.kjim.org/upload/pdf/kjim-2018-349.pdf |
| Summary: | 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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| ISSN: | 1226-3303 2005-6648 |