ABSTRACT
CoViD-19 pandemic has shown that we have deep gaps in understanding this extremely infectious virus - not only both from a clinical diagnosis and treatment perspective - but also from a forecasting point of view, so that we are better prepared for the next onset of a similar pandemic, which, at this point, seems almost inevitable. In this paper, we present a novel approach towards modeling influenza, a closely related disease to CoViD-19, marrying clinical understanding with artificial intelligence, exploiting the Forest Deep Neural Network (fDNN) with accuracy rates in the 90% range. © 2021 IEEE.
ABSTRACT
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. © 2020 IEEE.