@inproceedings{e6cc9e08d9d24fd98662b4da04b11c50,
title = "Using binary logistic regression coefficients for the dynamic quantification of comorbidities",
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.",
keywords = "Binary logistic regression, Comorbidity, Healthcare",
author = "Dimitrios Zikos and Ismail Vandeliwala",
year = "2015",
month = jul,
day = "1",
doi = "10.1145/2769493.2775131",
language = "English",
series = "8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2015 - Proceedings",
publisher = "Association for Computing Machinery, Inc",
booktitle = "8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2015 - Proceedings",
note = "null ; Conference date: 01-07-2015 Through 03-07-2015",
}