Deep learning methods to predict mortality in Covid-19 patients: A rapid scoping review

Mahanazuddin Syed, Shorabuddin Syed, Kevin Sexton, Melody L. Greer, Meredith Zozus, Sudeepa Bhattacharyya, Farhanuddin Syed, Fred Prior

Research output: Contribution to journalReview articlepeer-review

Abstract

The ongoing COVID-19 pandemic has become the most impactful pandemic of the past century. The SARS-CoV-2 virus has spread rapidly across the globe affecting and straining global health systems. More than 2 million people have died from COVID-19 (as of 30 January 2021). To lessen the pandemic's impact, advanced methods such as Artificial Intelligence models are proposed to predict mortality, morbidity, disease severity, and other outcomes and sequelae. We performed a rapid scoping literature review to identify the deep learning techniques that have been applied to predict hospital mortality in COVID-19 patients. Our review findings provide insights on the important deep learning models, data types, and features that have been reported in the literature. These summary findings will help scientists build reliable and accurate models for better intervention strategies for predicting mortality in current and future pandemic situations.

Original languageEnglish (US)
Pages (from-to)799-803
Number of pages5
JournalStudies in Health Technology and Informatics
Volume281
DOIs
StatePublished - 2021

Keywords

  • COVID-19
  • Death
  • Deep Learning
  • Mortality
  • Pandemic
  • Scoping Review

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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