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01715nam a2200373Ia 4500 |
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10.3233-SHTI220789 |
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220718s2022 CNT 000 0 und d |
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|a 18798365 (ISSN)
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|a A Machine Learning Solution to Predict Elective Orthopedic Surgery Case Duration
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|b NLM (Medline)
|c 2022
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|z View Fulltext in Publisher
|u https://doi.org/10.3233/SHTI220789
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|a 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.
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|a adult
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|a article
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|a climate model
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|a controlled study
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|a demography
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|a elective surgery
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|a female
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|a Machine learning
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|a major clinical study
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|a male
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|a operation duration
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|a orthopedic surgery
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|a Predictive Modelling
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|a random forest
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|a Surgery Case Duration
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|a trust
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|a Kunz, H.
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|a Lovegrove, T.
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|a Sahadev, D.
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|t Studies in health technology and informatics
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