Please use this identifier to cite or link to this item: http://hdl.handle.net/11434/1816
Title: Predicting unplanned hospital readmissions.
Authors: Wickramasinghe, Nilmini
Manuet, Day
Shuakat, Muhammad Nadeem
McConchie, Steven
Other Authors: Reischl, D.
Eigner, Isabella
Bodendorf, Freimut
Keywords: Unplanned Hospital Readmissions
Hysterectomies
Prediction Model
Decision Tree
DT
Artificial Neural Net
ANN
Support Vector Machine
SVM
AUC-Score
Boosting
Bagging
RUSBoost
Underbagging
SMOTEBoost
Overbagging
Unplanned Hysterectomy Readmissions
Predictive Models
Novel Ensemble Approach
Predictive Performance
Chair of Health Informatics Management, Epworth HealthCare, Victoria, Australia
Obstetrics and Gynaecology Clinical Institute, Epworth HealthCare, Victoria, Australia
Issue Date: Aug-2019
Conference Name: Epworth HealthCare Research Week 2019
Conference Location: Epworth Research Institute, Victoria, Australia
Abstract: INTRODUCTION: Unplanned hospital readmissions are quality of care indicators and major cost drivers. This study focuses on understanding unplanned readmissions in the case of hysterectomies by setting out to develop a suitable predictive model.
URI: http://hdl.handle.net/11434/1816
Type: Conference Poster
Affiliated Organisations: Friedrich-Alexander-Universitat Erlangen, Nurnberg, Germany
Swinburne University, Melbourne, Australia
Type of Clinical Study or Trial: Comparative Study
Appears in Collections:Research Week

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