PropertyValue
?:abstract
  • 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.
?:creator
?:doi
  • 10.1007/s10916-020-01562-1
?:doi
?:journal
  • J_Med_Syst
?:license
  • no-cc
?:pdf_json_files
  • document_parses/pdf_json/2bf5b025bedb949f46283ce59a820a324fd5d7d8.json
?:pmc_json_files
  • document_parses/pmc_json/PMC7087612.xml.json
?:pmcid
?:pmid
?:pmid
  • 32189081.0
?:publication_isRelatedTo_Disease
?:sha_id
?:source
  • Medline; PMC
?:title
  • AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data
?:type
?:year
  • 2020-03-18

Metadata

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