Abstract
This paper presents a novel Lasso Logistic Regression model based on feature-based time series data to determine disease severity and when to administer drugs or escalate intervention procedures in patients with coronavirus disease 2019 (COVID-19). Advanced features were extracted from highly enriched and time series vital sign data of hospitalized COVID-19 patients, including oxygen saturation readings, and with a combination of patient demographic and comorbidity information, as inputs into the dynamic feature-based classification model. Such dynamic combinations brought deep insights to guide clinical decision-making of complex COVID-19 cases, including prognosis prediction, timing of drug administration, admission to intensive care units, and application of intervention procedures like ventilation and intubation. The COVID-19 patient classification model was developed utilizing 900 hospitalized COVID-19 patients in a leading multi-hospital system in Texas, United States. By providing mortality prediction based on time-series physiologic data, demographics, and clinical records of individual COVID-19 patients, the dynamic feature-based classification model can be used to improve efficacy of the COVID-19 patient treatment, prioritize medical resources, and reduce casualties. The uniqueness of our model is that it is based on just the first 24 hours of vital sign data such that clinical interventions can be decided early and applied effectively. Such a strategy could be extended to prioritize resource allocations and drug treatment for futurepandemic events.
Highlights
Coronavirus disease disease caused by2019 (COVID-19) is an severe acute respiratory infectious syndrome coronavirus 2 (SARS-Cov-2)
Our research findings indicate that SpO2 patterns aided to differentiate expired and discharged patients, and indicated the potential of the utilization of the model in clinical decision-making regarding intensive care unit (ICU) admissions and timing of starting certain known drugs such as tocilizumab
In the presented Lasso logistic regression model, all other features kept in the model are the extracted features from time-series vital signs
Summary
2019 (COVID-19) is an severe acute respiratory infectious syndrome coronavirus 2 (SARS-Cov-2). The prevention and testing strategy for COVID-19 is outside the scope of this work, it is worth noting that the public health policies and plans of many countries, especially during early stages of the pandemic, calculate the fear of overwhelming available health systems. Our research aims to improve the classification models by finding more useful features just from information available at early stage, like at patient’s admission, to go beyond clinical outcomes and tackle the multiple layers of complex decision-making in the care of in-hospital COVID-19 patients. Hospitals are taking vital signs like temperature, blood pressure, and oxygen saturation levels for COVID-19 patients, even measuring as frequently as at intervals of ten or fifteen minutes, only to make decisions based on the latest measurement or the few most recently sampled readings, while neglecting indication to outcomes hidden in the trend of vital sign changes over extended time. Hyperinflammatory state, predominantly affecting the lungs, causing acute respiratory distress syndrome, a critical condition that often leads to the sudden death of COVID-19 patients
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
