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• W4383216968 abstract "In response to the unprecedented global healthcare crisis of the COVID-19 pandemic, the scientific community has joined forces to tackle the challenges and prepare for future pandemics. Multiple modalities of data have been investigated to understand the nature of COVID-19. In this paper, MIDRC investigators present an overview of the state-of-the-art development of multimodal machine learning for COVID-19 and model assessment considerations for future studies. We begin with a discussion of the lessons learned from radiogenomic studies for cancer diagnosis. We then summarize the multi-modality COVID-19 data investigated in the literature including symptoms and other clinical data, laboratory tests, imaging, pathology, physiology, and other omics data. Publicly available multimodal COVID-19 data provided by MIDRC and other sources are summarized. After an overview of machine learning developments using multimodal data for COVID-19, we present our perspectives on the future development of multimodal machine learning models for COVID-19." @default.
• W4383216968 created "2023-07-06" @default.
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• W4383216968 date "2023-07-01" @default.
• W4383216968 modified "2023-10-14" @default.
• W4383216968 title "Machine learning with multimodal data for COVID-19" @default.
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• W4383216968 doi "https://doi.org/10.1016/j.heliyon.2023.e17934" @default.
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