期刊
JOURNAL OF MEDICAL SYSTEMS
卷 44, 期 5, 页码 -出版社
SPRINGER
DOI: 10.1007/s10916-020-01562-1
关键词
COVID-19; Artificial intelligence; Machine learning; Active learning; Cross-population train; test models; Multitudinal and multimodal data
The novel coronavirus (COVID-19) outbreak, which was identified in late 2019, requires special attention because of its future epidemics and possible global threats. Beside clinical procedures and treatments, since Artificial Intelligence (AI) promises a new paradigm for healthcare, several different AI tools that are built upon Machine Learning (ML) algorithms are employed for analyzing data and decision-making processes. This means that AI-driven tools help identify COVID-19 outbreaks as well as forecast their nature of spread across the globe. However, unlike other healthcare issues, for COVID-19, to detect COVID-19, AI-driven tools are expected to have active learning-based cross-population train/test models that employs multitudinal and multimodal data, which is the primary purpose of the paper.
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