Predicting 30-day Readmission in Heart Failure using Machine Learning Techniques
Egileak: Nekane Larburu Rubio Vanessa Escolar Ainara Lozano
Data: 20.01.2018
Abstract
Heart Failure (HF) is a syndrome that reduces patients’ quality of life, and has severe impacts on healthcare systems worldwide, such as the high rate of readmissions. In order to reduce the readmissions and improve patients’ quality of life, several studies are trying to assess the risk of a patient to be readmitted, so that taking right actions clinicians can prevent patient deterioration and readmission. Predictive models have the ability to identify patients at high risk. Henceforth, this paper studies predictive models to determine the risk of a HF patient to be readmitted in the next 30 days after discharge. We present two different approaches. In the first one, we combine unsupervised and supervised classification and achieved AUC score of 0.64. In the second one, we combine decision tree and Naïve Bayes classifiers and achieved AUC score of 0.61. Additionally, we discover that the results improve when training the predictive models with different readmission’s threshold outcom e, reaching the AUC score of 0.73 when applying the first approach.
BIB_text
title = {Predicting 30-day Readmission in Heart Failure using Machine Learning Techniques},
pages = {308-315},
keywds = {
Heart Failure, Machine Learning, Hospital Readmission, Risk Prediction, Classification.
}
abstract = {
Heart Failure (HF) is a syndrome that reduces patients’ quality of life, and has severe impacts on healthcare systems worldwide, such as the high rate of readmissions. In order to reduce the readmissions and improve patients’ quality of life, several studies are trying to assess the risk of a patient to be readmitted, so that taking right actions clinicians can prevent patient deterioration and readmission. Predictive models have the ability to identify patients at high risk. Henceforth, this paper studies predictive models to determine the risk of a HF patient to be readmitted in the next 30 days after discharge. We present two different approaches. In the first one, we combine unsupervised and supervised classification and achieved AUC score of 0.64. In the second one, we combine decision tree and Naïve Bayes classifiers and achieved AUC score of 0.61. Additionally, we discover that the results improve when training the predictive models with different readmission’s threshold outcom e, reaching the AUC score of 0.73 when applying the first approach.
}
isbn = {978-989-758-281-3},
doi = {10.5220/0006542103080315},
date = {2018-01-20},
}
