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Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study

The scheduling of operating room (OR) slots requires the accurate prediction of surgery duration. We evaluated the performance of existing Moving Average (MA) based estimates with novel machine learning (ML)-based models of surgery durations across two sites in the US and Singapore. We used the Duke...

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Autores principales: Lam, Sean Shao Wei, Zaribafzadeh, Hamed, Ang, Boon Yew, Webster, Wendy, Buckland, Daniel, Mantyh, Christopher, Tan, Hiang Khoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319102/
https://www.ncbi.nlm.nih.gov/pubmed/35885718
http://dx.doi.org/10.3390/healthcare10071191
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author Lam, Sean Shao Wei
Zaribafzadeh, Hamed
Ang, Boon Yew
Webster, Wendy
Buckland, Daniel
Mantyh, Christopher
Tan, Hiang Khoon
author_facet Lam, Sean Shao Wei
Zaribafzadeh, Hamed
Ang, Boon Yew
Webster, Wendy
Buckland, Daniel
Mantyh, Christopher
Tan, Hiang Khoon
author_sort Lam, Sean Shao Wei
collection PubMed
description The scheduling of operating room (OR) slots requires the accurate prediction of surgery duration. We evaluated the performance of existing Moving Average (MA) based estimates with novel machine learning (ML)-based models of surgery durations across two sites in the US and Singapore. We used the Duke Protected Analytics Computing Environment (PACE) to facilitate data-sharing and big data analytics across the US and Singapore. Data from all colorectal surgery patients between 1 January 2012 and 31 December 2017 in Singapore and, 1 January 2015 to 31 December 2019 in the US were used, and 7585 cases and 3597 single and multiple procedure cases from Singapore and US were included. The ML models were based on categorical gradient boosting (CatBoost) models trained on common data fields shared by both institutions. The procedure codes were based on the Table of Surgical Procedure (TOSP) (Singapore) and the Current Procedural Terminology (CPT) codes (US). The two types of codes were mapped by surgical experts. The CPT codes were then transformed into the relative value unit (RVU). The ML models outperformed the baseline MA models. The MA, scheduled durations and procedure codes were found to have higher loadings as compared to surgeon factors. We further demonstrated the use of the Duke PACE in facilitating data-sharing and big data analytics.
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spelling pubmed-93191022022-07-27 Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study Lam, Sean Shao Wei Zaribafzadeh, Hamed Ang, Boon Yew Webster, Wendy Buckland, Daniel Mantyh, Christopher Tan, Hiang Khoon Healthcare (Basel) Article The scheduling of operating room (OR) slots requires the accurate prediction of surgery duration. We evaluated the performance of existing Moving Average (MA) based estimates with novel machine learning (ML)-based models of surgery durations across two sites in the US and Singapore. We used the Duke Protected Analytics Computing Environment (PACE) to facilitate data-sharing and big data analytics across the US and Singapore. Data from all colorectal surgery patients between 1 January 2012 and 31 December 2017 in Singapore and, 1 January 2015 to 31 December 2019 in the US were used, and 7585 cases and 3597 single and multiple procedure cases from Singapore and US were included. The ML models were based on categorical gradient boosting (CatBoost) models trained on common data fields shared by both institutions. The procedure codes were based on the Table of Surgical Procedure (TOSP) (Singapore) and the Current Procedural Terminology (CPT) codes (US). The two types of codes were mapped by surgical experts. The CPT codes were then transformed into the relative value unit (RVU). The ML models outperformed the baseline MA models. The MA, scheduled durations and procedure codes were found to have higher loadings as compared to surgeon factors. We further demonstrated the use of the Duke PACE in facilitating data-sharing and big data analytics. MDPI 2022-06-25 /pmc/articles/PMC9319102/ /pubmed/35885718 http://dx.doi.org/10.3390/healthcare10071191 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lam, Sean Shao Wei
Zaribafzadeh, Hamed
Ang, Boon Yew
Webster, Wendy
Buckland, Daniel
Mantyh, Christopher
Tan, Hiang Khoon
Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study
title Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study
title_full Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study
title_fullStr Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study
title_full_unstemmed Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study
title_short Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study
title_sort estimation of surgery durations using machine learning methods-a cross-country multi-site collaborative study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319102/
https://www.ncbi.nlm.nih.gov/pubmed/35885718
http://dx.doi.org/10.3390/healthcare10071191
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