Session Detail Information
Add this session to your itinerary

Cluster :  Health Applications

Session Information  : Sunday Nov 01, 11:00 - 12:30

Title:  Healthcare Data Analytics
Chair: Donald Lee,Yale School of Management, 165 Whitney Ave, New Haven CT, United States of America, donald.lee@yale.edu

Abstract Details

Title: A Statistical Approach to Cost-effectiveness Analysis under Uncertainty about the Willingness-to-pay
 Presenting Author: Reza Yaesoubi,Yale School of Public Health, 60 College Street, New Haven CT 06510, United States of America, reza.yaesoubi@yale.edu
 Co-Author: Forrest Crawford,Yale School of Public Health, 60 College Street, New Haven CT 06510, United States of America, forrest.crawfold@yale.edu
 David Paltiel,Yale School of Public Health, 60 College St., New Haven, United States of America, david.paltiel@yale.edu
 
Abstract: Although it plays a central role in cost-effectiveness analysis, societies’ willingness to invest for an additional unit of health is rarely known to policy makers. In this work, we develop a statistical model to help decision makers determine whether a new healthcare alternative is considered cost-effective in the absence of exact value for the willingness-to-pay for health.
  
Title: Networks Classification Via Mathematical Programming
 Presenting Author: Daehan Won,University of Washington, Seattle, 1415 NE Ravenna Blvd, #401, Seattle WA 98105, United States of America, wondae@uw.edu
 
Abstract: We are developing mathematical programming models to classify the network structured data. Along the line with the feature selection, we present node selection approach to increase the classification accuracy as well as improving interpretability. To verify the utility of our proposed approach, we demonstrate the result of brain functional connectivity network data set.
  
Title: Outcome-driven Personalized Treatment Design for Managing Diabetes
 Presenting Author: Eva Lee,Georgia Institute of Technology, eva.lee@gatech.edu
 
Abstract: This work is joint with Grady Memorial Hospital and the Atlanta VA Medical Center. We discuss an evidence-based decision support tool that couples a treatment predictive model with a planning model. Specifically, the predictive model uncovers drug effect based on pharmaco-kinetics and dynamics analysis. This evidence is then modeled within the personalized planning model for optimal treatment plan design. Results for a collection of patients will be presented.