Predicting Criticality in COVID-19 Patients
| Autor: | Temiloluwa Prioleau, Roger A. Hallman, Anjali Chikkula |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Decision support system
Prognosis prediction Coronavirus disease 2019 (COVID-19) business.industry 030208 emergency & critical care medicine medicine.disease 03 medical and health sciences 0302 clinical medicine Intensive care Pandemic Clinical staff Medicine Statistical analysis 030212 general & internal medicine Medical emergency Rural area business |
| Zdroj: | BCB Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics |
| DOI: | 10.1145/3388440.3412463 |
| Popis: | The COVID-19 pandemic has infected millions of people around the world, spreading rapidly and causing a flood of patients that risk overwhelming clinical facilities. Whether in urban or rural areas, hospitals have limited resources and personnel to treat critical infections in intensive care units, which must be allocated effectively. To assist clinical staff in deciding which patients are in the greatest need of critical care, we develop a predictive model based on a publicly-available data set that is rich in clinical markers. We perform statistical analysis to determine which clinical markers strongly correlate with hospital admission, semi-intensive care, and intensive care for COVID-19 patients. We create a predictive model that will assist clinical personnel in determining COVID-19 patient prognosis. Additionally, we take a step towards a global framework for COVID-19 prognosis prediction by incorporating statistical data for geographically and ethnically diverse COVID--19 patient sets into our own model. To the best of our knowledge, this is the first model which does not exclusively utilize local data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|