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http://hdl.handle.net/11434/1816| Title: | Predicting unplanned hospital readmissions. |
| Epworth 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 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Nilmini Wickramasinghe 1.pdf | 148.66 kB | Adobe PDF |  View/Open |
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