A Machine Learning Based Modeling of the Cytokine Storm as it Relates to COVID-19 Using a Virtual Clinical Semantic Network (vCSN)

This paper presents a targeted, machine learning based solution to model the phenomenon known as the 'cytokine storm,' which is suspected to play a major role in explaining the highly variable severity of COVID-19 among patients. It describes how a Natural Language Processing (NLP) approac...

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Bibliographic Details
Published in2020 IEEE International Conference on Big Data (Big Data) pp. 3803 - 3810
Main Authors Rahman, Abrar, Kriak, John, Meyer, Rick, Goldblatt, Sidney, Rahman, Fuad
Format Conference Proceeding
LanguageEnglish
Published IEEE 10.12.2020
Subjects
Big data
Clustering
Coronavirus
Correlation
COVID-19
Data models
Healthcare
Machine learning
Modeling
Natural language processing
Storms
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Summary:This paper presents a targeted, machine learning based solution to model the phenomenon known as the 'cytokine storm,' which is suspected to play a major role in explaining the highly variable severity of COVID-19 among patients. It describes how a Natural Language Processing (NLP) approach, augmented by biomedical knowledge databases, can extract pre-existing conditions and relevant clinical markers from Electronic Health Records (EHRs). These extracted variables can be modeled to demonstrate correlation with the severity of infection outcomes, the building blocks of a comprehensive risk assessment and stratification strategy to predict which patients have higher or lower risks in terms of the disease severity and likelihood of hospitalization, exclusively from insights taken from the natural language data. The model has been applied to a cohort of patients from a large database of real, anonymized patients and has displayed demonstrable results.
DOI:10.1109/BigData50022.2020.9378284