Name
Abstract | ||
|---|---|---|
The COVID-19 Healthcare Coalition was established as a private sector-led response to the COVID-19 pandemic. Its purpose was to bring together healthcare organizations, technology firms, nonprofits, academia, and startups to preserve the healthcare delivery system and help protect U.S. populations by providing data-driven, real-time insights that improve outcomes. This required the coalition to obtain, align, and orchestrate many heterogeneous data sources and present this data on dashboards in a format that was understandable and useful to decision makers. To do this, the coalition employed an ensemble approach to analysis, combining machine learning algorithms together with theory-based simulations, allowing prognosis to provide computational decision support rooted in science and engineering. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/MCSE.2020.3036586 | Computing in Science & Engineering |
Keywords | DocType | Volume |
computational decision support,private sector-led response,COVID-19 pandemic,healthcare organizations,healthcare delivery system,heterogeneous data sources,COVID-19 healthcare coalition,US populations,theory-based simulations | Journal | 23 |
Issue | ISSN | Citations |
1 | 1521-9615 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Osman Balci | 1 | 872 | 175.02 |
| Christoper Glazner | 2 | 0 | 0.34 |
| Joseph Ungerleider | 3 | 0 | 0.34 |