Using binary logistic regression coefficients for the dynamic quantification of comorbidities

Dimitrios Zikos, Ismail Vandeliwala

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Comorbidities are multiple co-occurring disorders related with a series of inpatient effects which affect the overall quality of care. We describe a methodology for the dynamic quantification of the effect of comorbidities on important health outcomes, such as the in-hospital mortality and patient complications. Using a comprehensive Medicare dataset, our algorithm utilizes the coefficients of binary logistic regression models to predict the impact of a comorbidity to a binary outcome and the effect of any new disease to this comorbidity profile. To demonstrate the functionality of the algorithm, we developed a pilot Java based web application. The system can be useful upon the first patient encounter as well as during the entire service episode of the care.

Original languageEnglish
Title of host publication8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2015 - Proceedings
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450334525
DOIs
StatePublished - Jul 1 2015
Event8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2015 - Corfu, Greece
Duration: Jul 1 2015Jul 3 2015

Publication series

Name8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2015 - Proceedings

Conference

Conference8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2015
Country/TerritoryGreece
CityCorfu
Period07/1/1507/3/15

Keywords

  • Binary logistic regression
  • Comorbidity
  • Healthcare

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