Big Data Analytics + Virtual Clinical Semantic Network (vCSN): An Approach to Addressing the Increasing Clinical Nuances and Organ Involvement of COVID-19

Fuad Rahman, Rick Meyer, John Kriak, Sidney Goldblatt, Marvin J. Slepian

Research output: Contribution to journalArticlepeer-review

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has revealed deep gaps in our understanding of the clinical nuances of this extremely infectious viral pathogen. In order for public health, care delivery systems, clinicians, and other stakeholders to be better prepared for the next wave of SARS-CoV-2 infections, which, at this point, seems inevitable, we need to better understand this disease-not only from a clinical diagnosis and treatment perspective-but also from a forecasting, planning, and advanced preparedness point of view. To predict the onset and outcomes of a next wave, we first need to understand the pathologic mechanisms and features of COVID-19 from the point of view of the intricacies of clinical presentation, to the nuances of response to therapy. Here, we present a novel approach to model COVID-19, utilizing patient data from related diseases, combining clinical understanding with artificial intelligence modeling. Our process will serve as a methodology for analysis of the data being collected in the ASAIO database and other data sources worldwide.

Original languageEnglish (US)
Pages (from-to)18-24
Number of pages7
JournalASAIO journal (American Society for Artificial Internal Organs : 1992)
Volume67
Issue number1
DOIs
StatePublished - Jan 1 2021

ASJC Scopus subject areas

  • Biophysics
  • Bioengineering
  • Biomaterials
  • Biomedical Engineering

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