Building a COVID-19 vulnerability index
| Autor: | Dave DeCaprio, Joseph Gartner, Carol J. McCall, Thadeus Burgess, Kristian Garcia, Sarthak Kothari, Shaayaan Sayed |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
J.3 Vulnerability index Coronavirus disease 2019 (COVID-19) Computer Science - Artificial Intelligence business.industry I.5.4 High mortality Acute respiratory disease Disease Statistics - Applications I.2.1 68T05 Outreach Artificial Intelligence (cs.AI) Environmental health Pandemic Medicine Applications (stat.AP) business Proxy (statistics) |
| Zdroj: | Journal of Medical Artificial Intelligence. 3:15-15 |
| ISSN: | 2617-2496 |
| Popis: | COVID-19 is an acute respiratory disease that has been classified as a pandemic by the World Health Organization. Information regarding this particular disease is limited, however, it is known to have high mortality rates, particularly among individuals with preexisting medical conditions. Creating models to identify individuals who are at the greatest risk for severe complications due to COVID-19 will be useful to help for outreach campaigns in mitigating the diseases worst effects. While information specific to COVID-19 is limited, a model using complications due to other upper respiratory infections can be used as a proxy to help identify those individuals who are at the greatest risk. We present the results for three models predicting such complications, with each model having varying levels of predictive effectiveness at the expense of ease of implementation. |
| Databáze: | OpenAIRE |
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