An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic

• URL: http://arxiv.org/abs/2111.14623v1
• Date: Thu, 25 Nov 2021 06:37:24 GMT
• Title: An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic
• Authors: Zhe Fei, Yevgen Ryeznik, Oleksandr Sverdlov, Chee Wei Tan and Weng Kee Wong
• Abstract summary: We describe how innovative analytical methods, machine learning tools and metaheuristics can tackle general healthcare problems. In particular, we give applications of modern digital technology, statistical methods, data platforms and data integration systems. We make the case that analyzing and interpreting big data is a very challenging task that requires a multi-disciplinary effort.
• Score: 20.912943922420407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the era of big data, standard analysis tools may be inadequate for making inference and there is a growing need for more efficient and innovative ways to collect, process, analyze and interpret the massive and complex data. We provide an overview of challenges in big data problems and describe how innovative analytical methods, machine learning tools and metaheuristics can tackle general healthcare problems with a focus on the current pandemic. In particular, we give applications of modern digital technology, statistical methods, data platforms and data integration systems to improve diagnosis and treatment of diseases in clinical research and novel epidemiologic tools to tackle infection source problems, such as finding Patient Zero in the spread of epidemics. We make the case that analyzing and interpreting big data is a very challenging task that requires a multi-disciplinary effort to continuously create more effective methodologies and powerful tools to transfer data information into knowledge that enables informed decision making.

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