Predictive modelling for hospital readmission risk in the Philippines
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Author (Corporation)
Publication date
2020
Typ of student thesis
Course of study
Collections
Type
04B - Conference paper
Editors
Editor (Corporation)
Supervisor
Parent work
Proceedings of 2nd international conference on applied computing
Special issue
DOI of the original publication
Link
Series
IOP conference series. Materials science and engineering
Series number
864
Volume
Issue / Number
Pages / Duration
411-416
Patent number
Publisher / Publishing institution
Curran Associates
Place of publication / Event location
Bangkok
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
Predictive models have been developed over the years to identify patients at risk of readmission. The goal of this study is to identify the risk factors associated to a patient’s readmission within one year in the cohort study including acute myocardial infarction (AMI), Heart Failure (HF), Chronic Obstructive Pulmonary Disease (COPD) and Pneumonia (PN) in a reputed Philippine hospital. Four predictive models were used and evaluated using performance metrics. The study found Logistic Regression as the most performing model in most of the cohort studies. There are 6 to 8 variables significantly associated with the readmission of high-risk patients.
Keywords
Subject (DDC)
Event
2nd Joint Conference on Green Engineering Technology & Applied Computing
Exhibition start date
Exhibition end date
Conference start date
04.02.2020
Conference end date
05.02.2020
Date of the last check
ISBN
978-1-7138-4736-6
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Hybrid
License
Citation
Landicho, J. A., Esichaikul, V., & Sasil, R. (2020). Predictive modelling for hospital readmission risk in the Philippines. Proceedings of 2nd International Conference on Applied Computing, 411–416. https://doi.org/10.1088/1757-899X/864/1/012061