Sahadev, Divya;
Lovegrove, Thomas;
Kunz, Holger;
(2022)
A Machine Learning Solution to Predict Elective Orthopedic Surgery Case Duration.
Studies in Health Technology and Informatics
, 295
pp. 559-561.
10.3233/SHTI220789.
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Abstract
We used surgery durations, patient demographic and personnel data taken from the East Kent Hospitals University NHS Foundation Trust (EKHUFT) over a period of 10 years (2010-2019) for a total of 25,352 patients that underwent 15 highest volume elective orthopedic surgeries, to predict future surgery durations for the subset of elective surgeries under consideration. As part of this study, we compared two different ensemble machine learning methods random forest regression (RF) and XGBoost (eXtreme Gradient Boosting) regression. The two models were approximately 5% superior to the existing model used by the hospital scheduling system.
| Type: | Article |
|---|---|
| Title: | A Machine Learning Solution to Predict Elective Orthopedic Surgery Case Duration. |
| Location: | Netherlands |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3233/SHTI220789 |
| Publisher version: | http://doi.org/10.3233/SHTI220789 |
| Language: | English |
| Additional information: | © 2022 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). |
| Keywords: | Machine learning, Predictive Modelling, Surgery Case Duration |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10151731 |
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