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Activity Number: 244 - Contributed Poster Presentations: Section on Medical Devices and Diagnostics
Type: Contributed
Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Medical Devices and Diagnostics
Abstract #324608
Title: Impact of Missing Data in Feature Selection
Author(s): Carmen Khoo*
Companies:
Keywords: Variable selection ; Missing data ; Electronic health records ; Clustering
Abstract:

Electronic health records such as the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS), which collects records on mechanical circulatory support devices (MCSDs), clinical events, and follow-up evaluations of patients with advanced heart failure, are becoming increasingly granular. To build the mortality models, we first identify pre-MCSD therapy variables that are highly predictive of the outcome of interest: post-operation patient survival. The presence of missing data introduces hurdles to variable selection, primarily bias. We explore several variable selection and clustering techniques on both numerical and categorical variables, and then compare the subsets of variables that are selected to investigate the effects of missing data in determining the predictors.


Authors who are presenting talks have a * after their name.

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