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Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk.

Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk. Yehia H. Khalil , University of Louisville, Louisville, KY,US. presented by: Xxxxxxx DSCI 5240. Aim Develop a predictive model to forecast future Asthma hospitalization Asthma

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Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk.

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  1. Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future hospitalization Risk. Yehia H. Khalil, University of Louisville, Louisville, KY,US presented by: Xxxxxxx DSCI 5240

  2. 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

  3. 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

  4. Source of 2009 Dataset • Medical Expenditure Panel Survey • California Health Interview Survey • Survey • 47,614 adults • 3,379 adolescents • 8,945 children

  5. 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

  6. Fig. 4 Analysis Diagram • note: • 40% training • 30% testing • 30% validation

  7. 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

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