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
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 outco
me, reaching the AUC score of 0.73 when applying the first approach.
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