Predicting emergency department events due to asthma : results from the BRFSS Asthma Call Back Survey 2006-2009
| dc.contributor.advisor | Meyers, Lauren Ancel | en |
| dc.contributor.committeeMember | Scott, James | en |
| dc.creator | Chancellor, Courtney Marie | en |
| dc.date.accessioned | 2012-12-05T15:21:54Z | en |
| dc.date.available | 2012-12-05T15:21:54Z | en |
| dc.date.issued | 2012-05 | en |
| dc.date.submitted | May 2012 | en |
| dc.date.updated | 2012-12-05T15:22:03Z | en |
| dc.description | text | en |
| dc.description.abstract | The identification of asthma patients most at risk of experiencing an emergency department event is an important step toward lessening public health burdens in the United States. In this report, the CDC BRFSS Asthma Call Back Survey Data from 2006 to 2009 is explored for potential factors for a predictive model. A metric for classifying the control level of asthma patients is constructed and applied. The data is then used to construct a predictive model for ED events with the rpart algorithm. | en |
| dc.description.department | Computational Science, Engineering, and Mathematics | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.slug | 2152/ETD-UT-2012-05-5551 | en |
| dc.identifier.uri | http://hdl.handle.net/2152/ETD-UT-2012-05-5551 | en |
| dc.language.iso | eng | en |
| dc.subject | Asthma | en |
| dc.subject | Predictive modeling | en |
| dc.subject | rpart | en |
| dc.subject | Regression trees | en |
| dc.title | Predicting emergency department events due to asthma : results from the BRFSS Asthma Call Back Survey 2006-2009 | en |
| dc.type.genre | thesis | en |
| thesis.degree.department | Computational Science, Engineering, and Mathematics | en |
| thesis.degree.discipline | Computational Science, Engineering, and Mathematics | en |
| thesis.degree.grantor | University of Texas at Austin | en |
| thesis.degree.level | Masters | en |
| thesis.degree.name | Master of Science in Computational Science, Engineering, and Mathematics | en |