AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data

• Published in: Journal of medical systems Vol. 44; no. 5; p. 93
• Main Author: Santosh, K. C.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2020; Springer Nature B.V
• Subjects: Active learning; Algorithms; Artificial Intelligence; Clinical decision making; Coronaviridae; Coronavirus Infections - diagnosis; Coronavirus Infections - epidemiology; Coronaviruses; COVID-19; Decision analysis; Decision Making; Delivery of Health Care; Disease Outbreaks; Education & Training; Epidemics; Forecasting; Health care; Health Informatics; Health Sciences; Humans; Learning algorithms; Machine Learning; Medicine; Medicine & Public Health; Outbreaks; Pneumonia, Viral - diagnosis; Pneumonia, Viral - epidemiology; Statistics for Life Sciences; Viral diseases; COVID-19; Multitudinal and multimodal data; Cross-population train/test models; Active learning; Artificial intelligence; Machine learning
• ISSN: 0148-5598; 1573-689X; 1573-689X
• DOI: 10.1007/s10916-020-01562-1

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.