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PROJECT TITLE :

A Time-Series Feature-Based Recursive Classification Model to Optimize Treatment Strategies for Improving Outcomes and Resource Allocations of COVID-19 Patients

ABSTRACT:

This paper presents a novel Lasso Logistic Regression model based on feature-based time series data to determine disease severity and when patients with coronavirus disease 2019 should administer drugs or escalate intervention procedures (COVID-19). In order to provide inputs for the dynamic feature-based classification model, advanced features were extracted from highly enriched and time series vital sign data of hospitalized COVID-19 patients. These advanced features included oxygen saturation readings, and they were combined with information regarding patient demographics and comorbidities. These dynamic combinations brought about profound insights that guided the clinical decision-making process for complex COVID-19 cases. These insights included the prediction of the prognosis, the timing of drug administration, admission to intensive care units, and the application of intervention procedures such as ventilation and intubation. The COVID-19 patient classification model was developed with the help of 900 COVID-19 patients who were hospitalized in a leading multi-hospital system in the state of Texas, which is located in the United States. The dynamic feature-based classification model can be used to improve the efficacy of the treatment given to COVID-19 patients, prioritize the use of medical resources, and reduce the number of casualties that result from the outbreak. This is accomplished by providing a mortality prediction based on time-series physiologic data, demographics, and clinical records of individual patients. Our model is one of a kind due to the fact that it is based solely on the first twenty-four hours' worth of vital sign data. As a result, decisions regarding clinical interventions can be made quickly and efficiently. A strategy like this could be expanded to prioritize the distribution of resources and the treatment of drugs in the event of future pandemics.


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