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Authors: Jon Kerexeta 1 ; Arkaitz Artetxe 1 ; Vanessa Escolar 2 ; Ainara Lozano 2 and Nekane Larburu 1

Affiliations: 1 Vicomtech-IK4 Research Centre and Biodonostia Health Research Institute, Spain ; 2 Hospital Universitario de Basurto (Osakidetza Health Care System), Spain

ISBN: 978-989-758-281-3

Keyword(s): Heart Failure, Machine Learning, Hospital Readmission, Risk Prediction, Classification.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Cardiovascular Technologies ; Computing and Telecommunications in Cardiology ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; Development of Assistive Technology ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Health Information Systems ; Knowledge-Based Systems ; Pattern Recognition and Machine Learning ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

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. (More)

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Paper citation in several formats:
Kerexeta, J.; Artetxe, A.; Escolar, V.; Lozano, A. and Larburu, N. (2018). Predicting 30-day Readmission in Heart Failure using Machine Learning Techniques.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-281-3, pages 308-315. DOI: 10.5220/0006542103080315

@conference{healthinf18,
author={Jon Kerexeta. and Arkaitz Artetxe. and Vanessa Escolar. and Ainara Lozano. and Nekane Larburu.},
title={Predicting 30-day Readmission in Heart Failure using Machine Learning Techniques},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,},
year={2018},
pages={308-315},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006542103080315},
isbn={978-989-758-281-3},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,
TI - Predicting 30-day Readmission in Heart Failure using Machine Learning Techniques
SN - 978-989-758-281-3
AU - Kerexeta, J.
AU - Artetxe, A.
AU - Escolar, V.
AU - Lozano, A.
AU - Larburu, N.
PY - 2018
SP - 308
EP - 315
DO - 10.5220/0006542103080315

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