Predicting 30-Day Emergency Readmission Risk
Egileak: Arkaitz Artetxe Vallejo, Andoni Beristain Iraola, Manuel Graña, Ariadna Besga
Data: 01.01.2017
Abstract
Objective: Predicting Emergency Department (ED) readmissions is of great importance since it helps identifying patients requiring further post-discharge attention as well as reducing healthcare costs. It is becoming standard procedure to evaluate the risk of ED readmission within 30 days after discharge. Methods. Our dataset is stratified into four groups according to the Kaiser Permanente Risk Stratification Model. We deal with imbalanced data using different approaches for resampling. Feature selection is also addressed by a wrapper method which evaluates feature set importance by the performance of various classifiers trained on them. Results. We trained a model for each scenario and subpopulation, namely case management (CM), heart failure (HF), chronic obstructive pulmonary disease (COPD) and diabetes mellitus (DM). Using the full dataset we found that the best sensitivity is achieved by SVM using over-sampling methods (40.62 % sensitivity, 78.71 % specificity and 71.94 accuracy). Conclusions. Imbalance correction techniques allow to achieve better sensitivity performance, however the dataset has not enough positive cases, hindering the achievement of better prediction ability. The arbitrary definition of a threshold-based discretization for measurements which are inherently is an important drawback for the exploitation of the data, therefore a regression approach is considered as future work.
BIB_text
author = {Arkaitz Artetxe Vallejo, Andoni Beristain Iraola, Manuel Graña, Ariadna Besga},
title = {Predicting 30-Day Emergency Readmission Risk},
pages = {3-12},
volume = {527},
keywds = {
Readmission risk, Imbalanced datasets, SVM, Classification
}
abstract = {
Objective: Predicting Emergency Department (ED) readmissions is of great importance since it helps identifying patients requiring further post-discharge attention as well as reducing healthcare costs. It is becoming standard procedure to evaluate the risk of ED readmission within 30 days after discharge. Methods. Our dataset is stratified into four groups according to the Kaiser Permanente Risk Stratification Model. We deal with imbalanced data using different approaches for resampling. Feature selection is also addressed by a wrapper method which evaluates feature set importance by the performance of various classifiers trained on them. Results. We trained a model for each scenario and subpopulation, namely case management (CM), heart failure (HF), chronic obstructive pulmonary disease (COPD) and diabetes mellitus (DM). Using the full dataset we found that the best sensitivity is achieved by SVM using over-sampling methods (40.62 % sensitivity, 78.71 % specificity and 71.94 accuracy). Conclusions. Imbalance correction techniques allow to achieve better sensitivity performance, however the dataset has not enough positive cases, hindering the achievement of better prediction ability. The arbitrary definition of a threshold-based discretization for measurements which are inherently is an important drawback for the exploitation of the data, therefore a regression approach is considered as future work.
}
isbn = {978-3-319-47363-5},
isi = {1},
doi = {10.1007/978-3-319-47364-2_1},
date = {2017-01-01},
year = {2017},
}
