788.11J Presentation “ Fire Wx Net ” Presented by Engy Ashraf Yehia.
Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk....
-
Upload
pierce-french -
Category
Documents
-
view
214 -
download
0
Transcript of Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk....
![Page 1: Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk. Yehia H. Khalil, University of Louisville, Louisville, KY,US.](https://reader030.fdocuments.us/reader030/viewer/2022032612/56649eb15503460f94bb6a55/html5/thumbnails/1.jpg)
Using SAS Predictive Modeling to Investigate the Asthma’s Patient
Future hospitalization Risk.Yehia H. Khalil, University of Louisville, Louisville, KY,US
presented by:
XxxxxxxDSCI 5240
![Page 2: Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk. Yehia H. Khalil, University of Louisville, Louisville, KY,US.](https://reader030.fdocuments.us/reader030/viewer/2022032612/56649eb15503460f94bb6a55/html5/thumbnails/2.jpg)
Aim
• Develop a predictive model to forecast future Asthma hospitalization
Asthma
• A chronic inflammatory disorder of the airways
• 21 million Americans diagnosed
• Hospitalization rate growing (more than a million cases a year)
• Costs for Asthma: $14 billion
![Page 3: Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk. Yehia H. Khalil, University of Louisville, Louisville, KY,US.](https://reader030.fdocuments.us/reader030/viewer/2022032612/56649eb15503460f94bb6a55/html5/thumbnails/3.jpg)
Predictive modeling
• Ability to incorporate any type of variable into analysis
• Dynamic; can easily accommodate any information to adjust model
SAS SEMMA methodology
• Sample
• Explore
• Modify
• Model
• Access
![Page 4: Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk. Yehia H. Khalil, University of Louisville, Louisville, KY,US.](https://reader030.fdocuments.us/reader030/viewer/2022032612/56649eb15503460f94bb6a55/html5/thumbnails/4.jpg)
Source of 2009 Dataset
• Medical Expenditure Panel Survey
• California Health Interview Survey
Survey
• 47,614 adults
• 3,379 adolescents
• 8,945 children
![Page 5: Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk. Yehia H. Khalil, University of Louisville, Louisville, KY,US.](https://reader030.fdocuments.us/reader030/viewer/2022032612/56649eb15503460f94bb6a55/html5/thumbnails/5.jpg)
Useful Parameters
• Demographics: age, race, marital status
• Health Behaviors: physical activities, fast food, alcohol consumption
• Health Conditions other than Asthma
• Health Insurance
• Poverty Level
• Emergency preparedness module: medication
• Mental or Emotional Condition
![Page 6: Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk. Yehia H. Khalil, University of Louisville, Louisville, KY,US.](https://reader030.fdocuments.us/reader030/viewer/2022032612/56649eb15503460f94bb6a55/html5/thumbnails/6.jpg)
![Page 7: Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk. Yehia H. Khalil, University of Louisville, Louisville, KY,US.](https://reader030.fdocuments.us/reader030/viewer/2022032612/56649eb15503460f94bb6a55/html5/thumbnails/7.jpg)
![Page 8: Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk. Yehia H. Khalil, University of Louisville, Louisville, KY,US.](https://reader030.fdocuments.us/reader030/viewer/2022032612/56649eb15503460f94bb6a55/html5/thumbnails/8.jpg)
Fig. 4 Analysis Diagram
note:
• 40% training
• 30% testing
• 30% validation
![Page 9: Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk. Yehia H. Khalil, University of Louisville, Louisville, KY,US.](https://reader030.fdocuments.us/reader030/viewer/2022032612/56649eb15503460f94bb6a55/html5/thumbnails/9.jpg)
![Page 10: Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk. Yehia H. Khalil, University of Louisville, Louisville, KY,US.](https://reader030.fdocuments.us/reader030/viewer/2022032612/56649eb15503460f94bb6a55/html5/thumbnails/10.jpg)
![Page 11: Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk. Yehia H. Khalil, University of Louisville, Louisville, KY,US.](https://reader030.fdocuments.us/reader030/viewer/2022032612/56649eb15503460f94bb6a55/html5/thumbnails/11.jpg)
![Page 12: Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk. Yehia H. Khalil, University of Louisville, Louisville, KY,US.](https://reader030.fdocuments.us/reader030/viewer/2022032612/56649eb15503460f94bb6a55/html5/thumbnails/12.jpg)
Conclusion
• General health conditions, psychological distress and poverty level
affect future hospitalization risk
• Rx coverage and patient disability influence taking medication
regularly and can increase future hospitalization risk
• It is possible to enhance interventions, programs and alternatives to
avoid future hospitalizations