Predicting emergency department events due to asthma : results from the BRFSS Asthma Call Back Survey 2006-2009

Predicting emergency department events due to asthma : results from the BRFSS Asthma Call Back Survey 2006-2009

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 pred...

Full description

Bibliographic Details
Main Author: Chancellor, Courtney Marie
Format: Others
Language:English
Published: 2012
Subjects:
Asthma
Predictive modeling
rpart
Regression trees
Online Access:http://hdl.handle.net/2152/ETD-UT-2012-05-5551
id ndltd-UTEXAS-oai-repositories.lib.utexas.edu-2152-ETD-UT-2012-05-5551
record_format oai_dc
spelling ndltd-UTEXAS-oai-repositories.lib.utexas.edu-2152-ETD-UT-2012-05-55512015-09-20T17:12:25ZPredicting emergency department events due to asthma : results from the BRFSS Asthma Call Back Survey 2006-2009Chancellor, Courtney MarieAsthmaPredictive modelingrpartRegression treesThe 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.text2012-12-05T15:21:54Z2012-12-05T15:21:54Z2012-052012-12-05May 20122012-12-05T15:22:03Zthesisapplication/pdfhttp://hdl.handle.net/2152/ETD-UT-2012-05-55512152/ETD-UT-2012-05-5551eng
collection NDLTD
language English
format Others
sources NDLTD
topic Asthma
Predictive modeling
rpart
Regression trees
spellingShingle Asthma
Predictive modeling
rpart
Regression trees
Chancellor, Courtney Marie
Predicting emergency department events due to asthma : results from the BRFSS Asthma Call Back Survey 2006-2009
description 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. === text
author Chancellor, Courtney Marie
author_facet Chancellor, Courtney Marie
author_sort Chancellor, Courtney Marie
title Predicting emergency department events due to asthma : results from the BRFSS Asthma Call Back Survey 2006-2009
title_short Predicting emergency department events due to asthma : results from the BRFSS Asthma Call Back Survey 2006-2009
title_full Predicting emergency department events due to asthma : results from the BRFSS Asthma Call Back Survey 2006-2009
title_fullStr Predicting emergency department events due to asthma : results from the BRFSS Asthma Call Back Survey 2006-2009
title_full_unstemmed Predicting emergency department events due to asthma : results from the BRFSS Asthma Call Back Survey 2006-2009
title_sort predicting emergency department events due to asthma : results from the brfss asthma call back survey 2006-2009
publishDate 2012
url http://hdl.handle.net/2152/ETD-UT-2012-05-5551
work_keys_str_mv AT chancellorcourtneymarie predictingemergencydepartmenteventsduetoasthmaresultsfromthebrfssasthmacallbacksurvey20062009
_version_ 1716822840953536512

Similar Items