Heart failure readmission or early death risk factor analysis: A case study in a telemonitoring program
Egileak: Nekane Larburu Rubio Nekane Murga Vanessa Escolar Manuel Graña
Data: 21.06.2018
Abstract
Heart Failure (HF) is a clinical syndrome caused by a structural and/or functional cardiac abnormality that imposes tremendous burden on patients and on the healthcare systems worldwide. In this context, predictive models may facilitate the identification of patients at high risk of death or unplanned hospital readmissions and potentially enable direct specific interventions. Currently a plethora of studies in this field is discussing whether hospital readmission and mortality can be effectively predicted in patients with HF. In this work, we present a preliminary study for identifying risk factors for unplanned readmission or death, using a clinical dataset with 119 patients and 60 features. Different classification algorithms and feature selection approaches were employed in order to increase the prediction ability of the models and reduce their complexity in terms of number of features. Results show that sequential feature selection methods along with SVM achieve the best scores in terms of accuracy for predicting 30-day readmission or death risk.
BIB_text
title = {Heart failure readmission or early death risk factor analysis: A case study in a telemonitoring program},
pages = {244-253},
keywds = {
Feature selection, heart failure, predictive models, readmission risk
}
abstract = {
Heart Failure (HF) is a clinical syndrome caused by a structural and/or functional cardiac abnormality that imposes tremendous burden on patients and on the healthcare systems worldwide. In this context, predictive models may facilitate the identification of patients at high risk of death or unplanned hospital readmissions and potentially enable direct specific interventions. Currently a plethora of studies in this field is discussing whether hospital readmission and mortality can be effectively predicted in patients with HF. In this work, we present a preliminary study for identifying risk factors for unplanned readmission or death, using a clinical dataset with 119 patients and 60 features. Different classification algorithms and feature selection approaches were employed in order to increase the prediction ability of the models and reduce their complexity in terms of number of features. Results show that sequential feature selection methods along with SVM achieve the best scores in terms of accuracy for predicting 30-day readmission or death risk.
}
isbn = {978-331959396-8},
doi = {10.1007/978-3-319-59397-5_26},
date = {2018-06-21},
}
