Leveraging machine learning for duration of surgery prediction in knee and hip arthroplasty – a development and validation study
Background Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patients using retrospective data available before surgery...
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| Vydané v: | BMC medical informatics and decision making Ročník 25; číslo 1; s. 106 - 13 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
BioMed Central
03.03.2025
BioMed Central Ltd Springer Nature B.V BMC |
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| ISSN: | 1472-6947, 1472-6947 |
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| Abstract | Background
Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patients using retrospective data available before surgery with reasonable performance, (2) to compare whether machine learning is able to outperform multivariable regression in predictive performance and (3) to identify the most important predictor variables for DOS both in a multi- and single-hospital context.
Methods
eXtreme Gradient Boosting (XGBoost) and multivariable linear regression were used for predictions. Both models were applied to both the whole dataset which included multiple hospitals (3,704 patients), and a single-hospital dataset (1,815 patients) of the hospital with the highest case-volumes of our sample. Data was split into training (75%) and test data (25%) for both datasets. Models were trained using 5-fold cross-validation (CV) on the training datasets and applied to test data for performance comparison.
Results
On test data in the multi-hospital setting, the mean absolute error (MAE) was 12.13 min (HA) / 13.61 min (KA) for XGBoost. In the single-hospital analysis, performance on test data was MAE 10.87 min (HA) / MAE 12.53 min (KA) for XGBoost. Predictive ability of XGBoost was tended to be better than of regression in all setting, however not statistically significantly. Important predictors for XGBoost were physician experience, age, body mass index, patient reported outcome measures and, for the multi-hospital analysis, the hospital.
Conclusion
Machine learning can predict DOS in both a multi-hospital and single-hospital setting with reasonable performance. Performance between regression and machine learning differed slightly, however insignificantly, while larger datasets may improve predictive performance. The study found that hospital indicators matter in the multi-hospital setting despite controlling for various variables, highlighting potential quality differences between hospitals.
Trial registration
The study was registered at the German Clinical Trials Register (DRKS) under DRKS00019916.
Highlights
Machine learning predicts surgery duration for hip and knee arthroplasty with a mean error of slightly above 10 min.
Performance was comparable for a multi-hospital and a single-hospital setting.
Hospital indicators were highly relevant predictors in the multi-hospital setting.
Patients age, body composition and surgery-team related variables were major drivers of predictive power in both settings. |
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| AbstractList | Abstract Background Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patients using retrospective data available before surgery with reasonable performance, (2) to compare whether machine learning is able to outperform multivariable regression in predictive performance and (3) to identify the most important predictor variables for DOS both in a multi- and single-hospital context. Methods eXtreme Gradient Boosting (XGBoost) and multivariable linear regression were used for predictions. Both models were applied to both the whole dataset which included multiple hospitals (3,704 patients), and a single-hospital dataset (1,815 patients) of the hospital with the highest case-volumes of our sample. Data was split into training (75%) and test data (25%) for both datasets. Models were trained using 5-fold cross-validation (CV) on the training datasets and applied to test data for performance comparison. Results On test data in the multi-hospital setting, the mean absolute error (MAE) was 12.13 min (HA) / 13.61 min (KA) for XGBoost. In the single-hospital analysis, performance on test data was MAE 10.87 min (HA) / MAE 12.53 min (KA) for XGBoost. Predictive ability of XGBoost was tended to be better than of regression in all setting, however not statistically significantly. Important predictors for XGBoost were physician experience, age, body mass index, patient reported outcome measures and, for the multi-hospital analysis, the hospital. Conclusion Machine learning can predict DOS in both a multi-hospital and single-hospital setting with reasonable performance. Performance between regression and machine learning differed slightly, however insignificantly, while larger datasets may improve predictive performance. The study found that hospital indicators matter in the multi-hospital setting despite controlling for various variables, highlighting potential quality differences between hospitals. Trial registration The study was registered at the German Clinical Trials Register (DRKS) under DRKS00019916. Background Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patients using retrospective data available before surgery with reasonable performance, (2) to compare whether machine learning is able to outperform multivariable regression in predictive performance and (3) to identify the most important predictor variables for DOS both in a multi- and single-hospital context. Methods eXtreme Gradient Boosting (XGBoost) and multivariable linear regression were used for predictions. Both models were applied to both the whole dataset which included multiple hospitals (3,704 patients), and a single-hospital dataset (1,815 patients) of the hospital with the highest case-volumes of our sample. Data was split into training (75%) and test data (25%) for both datasets. Models were trained using 5-fold cross-validation (CV) on the training datasets and applied to test data for performance comparison. Results On test data in the multi-hospital setting, the mean absolute error (MAE) was 12.13 min (HA) / 13.61 min (KA) for XGBoost. In the single-hospital analysis, performance on test data was MAE 10.87 min (HA) / MAE 12.53 min (KA) for XGBoost. Predictive ability of XGBoost was tended to be better than of regression in all setting, however not statistically significantly. Important predictors for XGBoost were physician experience, age, body mass index, patient reported outcome measures and, for the multi-hospital analysis, the hospital. Conclusion Machine learning can predict DOS in both a multi-hospital and single-hospital setting with reasonable performance. Performance between regression and machine learning differed slightly, however insignificantly, while larger datasets may improve predictive performance. The study found that hospital indicators matter in the multi-hospital setting despite controlling for various variables, highlighting potential quality differences between hospitals. Trial registration The study was registered at the German Clinical Trials Register (DRKS) under DRKS00019916. Keywords: Duration of surgery, Machine learning, Patient-reported outcome measures Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patients using retrospective data available before surgery with reasonable performance, (2) to compare whether machine learning is able to outperform multivariable regression in predictive performance and (3) to identify the most important predictor variables for DOS both in a multi- and single-hospital context.BACKGROUNDDuration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patients using retrospective data available before surgery with reasonable performance, (2) to compare whether machine learning is able to outperform multivariable regression in predictive performance and (3) to identify the most important predictor variables for DOS both in a multi- and single-hospital context.eXtreme Gradient Boosting (XGBoost) and multivariable linear regression were used for predictions. Both models were applied to both the whole dataset which included multiple hospitals (3,704 patients), and a single-hospital dataset (1,815 patients) of the hospital with the highest case-volumes of our sample. Data was split into training (75%) and test data (25%) for both datasets. Models were trained using 5-fold cross-validation (CV) on the training datasets and applied to test data for performance comparison.METHODSeXtreme Gradient Boosting (XGBoost) and multivariable linear regression were used for predictions. Both models were applied to both the whole dataset which included multiple hospitals (3,704 patients), and a single-hospital dataset (1,815 patients) of the hospital with the highest case-volumes of our sample. Data was split into training (75%) and test data (25%) for both datasets. Models were trained using 5-fold cross-validation (CV) on the training datasets and applied to test data for performance comparison.On test data in the multi-hospital setting, the mean absolute error (MAE) was 12.13 min (HA) / 13.61 min (KA) for XGBoost. In the single-hospital analysis, performance on test data was MAE 10.87 min (HA) / MAE 12.53 min (KA) for XGBoost. Predictive ability of XGBoost was tended to be better than of regression in all setting, however not statistically significantly. Important predictors for XGBoost were physician experience, age, body mass index, patient reported outcome measures and, for the multi-hospital analysis, the hospital.RESULTSOn test data in the multi-hospital setting, the mean absolute error (MAE) was 12.13 min (HA) / 13.61 min (KA) for XGBoost. In the single-hospital analysis, performance on test data was MAE 10.87 min (HA) / MAE 12.53 min (KA) for XGBoost. Predictive ability of XGBoost was tended to be better than of regression in all setting, however not statistically significantly. Important predictors for XGBoost were physician experience, age, body mass index, patient reported outcome measures and, for the multi-hospital analysis, the hospital.Machine learning can predict DOS in both a multi-hospital and single-hospital setting with reasonable performance. Performance between regression and machine learning differed slightly, however insignificantly, while larger datasets may improve predictive performance. The study found that hospital indicators matter in the multi-hospital setting despite controlling for various variables, highlighting potential quality differences between hospitals.CONCLUSIONMachine learning can predict DOS in both a multi-hospital and single-hospital setting with reasonable performance. Performance between regression and machine learning differed slightly, however insignificantly, while larger datasets may improve predictive performance. The study found that hospital indicators matter in the multi-hospital setting despite controlling for various variables, highlighting potential quality differences between hospitals.The study was registered at the German Clinical Trials Register (DRKS) under DRKS00019916.TRIAL REGISTRATIONThe study was registered at the German Clinical Trials Register (DRKS) under DRKS00019916. Background Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patients using retrospective data available before surgery with reasonable performance, (2) to compare whether machine learning is able to outperform multivariable regression in predictive performance and (3) to identify the most important predictor variables for DOS both in a multi- and single-hospital context. Methods eXtreme Gradient Boosting (XGBoost) and multivariable linear regression were used for predictions. Both models were applied to both the whole dataset which included multiple hospitals (3,704 patients), and a single-hospital dataset (1,815 patients) of the hospital with the highest case-volumes of our sample. Data was split into training (75%) and test data (25%) for both datasets. Models were trained using 5-fold cross-validation (CV) on the training datasets and applied to test data for performance comparison. Results On test data in the multi-hospital setting, the mean absolute error (MAE) was 12.13 min (HA) / 13.61 min (KA) for XGBoost. In the single-hospital analysis, performance on test data was MAE 10.87 min (HA) / MAE 12.53 min (KA) for XGBoost. Predictive ability of XGBoost was tended to be better than of regression in all setting, however not statistically significantly. Important predictors for XGBoost were physician experience, age, body mass index, patient reported outcome measures and, for the multi-hospital analysis, the hospital. Conclusion Machine learning can predict DOS in both a multi-hospital and single-hospital setting with reasonable performance. Performance between regression and machine learning differed slightly, however insignificantly, while larger datasets may improve predictive performance. The study found that hospital indicators matter in the multi-hospital setting despite controlling for various variables, highlighting potential quality differences between hospitals. Trial registration The study was registered at the German Clinical Trials Register (DRKS) under DRKS00019916. Highlights Machine learning predicts surgery duration for hip and knee arthroplasty with a mean error of slightly above 10 min. Performance was comparable for a multi-hospital and a single-hospital setting. Hospital indicators were highly relevant predictors in the multi-hospital setting. Patients age, body composition and surgery-team related variables were major drivers of predictive power in both settings. Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patients using retrospective data available before surgery with reasonable performance, (2) to compare whether machine learning is able to outperform multivariable regression in predictive performance and (3) to identify the most important predictor variables for DOS both in a multi- and single-hospital context. eXtreme Gradient Boosting (XGBoost) and multivariable linear regression were used for predictions. Both models were applied to both the whole dataset which included multiple hospitals (3,704 patients), and a single-hospital dataset (1,815 patients) of the hospital with the highest case-volumes of our sample. Data was split into training (75%) and test data (25%) for both datasets. Models were trained using 5-fold cross-validation (CV) on the training datasets and applied to test data for performance comparison. On test data in the multi-hospital setting, the mean absolute error (MAE) was 12.13 min (HA) / 13.61 min (KA) for XGBoost. In the single-hospital analysis, performance on test data was MAE 10.87 min (HA) / MAE 12.53 min (KA) for XGBoost. Predictive ability of XGBoost was tended to be better than of regression in all setting, however not statistically significantly. Important predictors for XGBoost were physician experience, age, body mass index, patient reported outcome measures and, for the multi-hospital analysis, the hospital. Machine learning can predict DOS in both a multi-hospital and single-hospital setting with reasonable performance. Performance between regression and machine learning differed slightly, however insignificantly, while larger datasets may improve predictive performance. The study found that hospital indicators matter in the multi-hospital setting despite controlling for various variables, highlighting potential quality differences between hospitals. The study was registered at the German Clinical Trials Register (DRKS) under DRKS00019916. BackgroundDuration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patients using retrospective data available before surgery with reasonable performance, (2) to compare whether machine learning is able to outperform multivariable regression in predictive performance and (3) to identify the most important predictor variables for DOS both in a multi- and single-hospital context.MethodseXtreme Gradient Boosting (XGBoost) and multivariable linear regression were used for predictions. Both models were applied to both the whole dataset which included multiple hospitals (3,704 patients), and a single-hospital dataset (1,815 patients) of the hospital with the highest case-volumes of our sample. Data was split into training (75%) and test data (25%) for both datasets. Models were trained using 5-fold cross-validation (CV) on the training datasets and applied to test data for performance comparison.ResultsOn test data in the multi-hospital setting, the mean absolute error (MAE) was 12.13 min (HA) / 13.61 min (KA) for XGBoost. In the single-hospital analysis, performance on test data was MAE 10.87 min (HA) / MAE 12.53 min (KA) for XGBoost. Predictive ability of XGBoost was tended to be better than of regression in all setting, however not statistically significantly. Important predictors for XGBoost were physician experience, age, body mass index, patient reported outcome measures and, for the multi-hospital analysis, the hospital.ConclusionMachine learning can predict DOS in both a multi-hospital and single-hospital setting with reasonable performance. Performance between regression and machine learning differed slightly, however insignificantly, while larger datasets may improve predictive performance. The study found that hospital indicators matter in the multi-hospital setting despite controlling for various variables, highlighting potential quality differences between hospitals.Trial registrationThe study was registered at the German Clinical Trials Register (DRKS) under DRKS00019916. Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patients using retrospective data available before surgery with reasonable performance, (2) to compare whether machine learning is able to outperform multivariable regression in predictive performance and (3) to identify the most important predictor variables for DOS both in a multi- and single-hospital context. eXtreme Gradient Boosting (XGBoost) and multivariable linear regression were used for predictions. Both models were applied to both the whole dataset which included multiple hospitals (3,704 patients), and a single-hospital dataset (1,815 patients) of the hospital with the highest case-volumes of our sample. Data was split into training (75%) and test data (25%) for both datasets. Models were trained using 5-fold cross-validation (CV) on the training datasets and applied to test data for performance comparison. On test data in the multi-hospital setting, the mean absolute error (MAE) was 12.13 min (HA) / 13.61 min (KA) for XGBoost. In the single-hospital analysis, performance on test data was MAE 10.87 min (HA) / MAE 12.53 min (KA) for XGBoost. Predictive ability of XGBoost was tended to be better than of regression in all setting, however not statistically significantly. Important predictors for XGBoost were physician experience, age, body mass index, patient reported outcome measures and, for the multi-hospital analysis, the hospital. Machine learning can predict DOS in both a multi-hospital and single-hospital setting with reasonable performance. Performance between regression and machine learning differed slightly, however insignificantly, while larger datasets may improve predictive performance. The study found that hospital indicators matter in the multi-hospital setting despite controlling for various variables, highlighting potential quality differences between hospitals. Machine learning predicts surgery duration for hip and knee arthroplasty with a mean error of slightly above 10 min. Performance was comparable for a multi-hospital and a single-hospital setting. Hospital indicators were highly relevant predictors in the multi-hospital setting. Patients age, body composition and surgery-team related variables were major drivers of predictive power in both settings. |
| ArticleNumber | 106 |
| Audience | Academic |
| Author | Langenberger, Benedikt Halder, Andreas Busse, Reinhard Schrednitzki, Daniel Pross, Christoph |
| Author_xml | – sequence: 1 givenname: Benedikt surname: Langenberger fullname: Langenberger, Benedikt email: benedikt.langenberger@hpi.de organization: Department of Health Care Management, Technische Universität Berlin, Chair of Digital Health, Economics & Policy, Hasso-Plattner-Institute – sequence: 2 givenname: Daniel surname: Schrednitzki fullname: Schrednitzki, Daniel organization: Department of Orthopaedic, Trauma, Hand and Reconstructive Surgery, Sana Klinikum Lichtenberg – sequence: 3 givenname: Andreas surname: Halder fullname: Halder, Andreas organization: Department of Orthopedic Surgery, Sana Klinken Sommerfeld – sequence: 4 givenname: Reinhard surname: Busse fullname: Busse, Reinhard organization: Department of Health Care Management, Technische Universität Berlin – sequence: 5 givenname: Christoph surname: Pross fullname: Pross, Christoph organization: Department of Health Care Management, Technische Universität Berlin |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40033378$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.artd.2021.01.006 10.1016/j.aap.2019.105405 10.1016/j.eclinm.2019.09.015 10.1214/aos/1013203451 10.1007/s00167-022-06957-w 10.1186/s12891-016-1025-8 10.1097/ALN.0b013e3181c294c2 10.1080/2573234X.2021.1873078 10.1302/0301-620X.101B6.BJJ-2018-1400.R1 10.1371/journal.pone.0273831 10.1016/B978-0-12-809633-8.20349-X 10.3390/bdcc6030076 10.1097/NNR.0000000000000602 10.3390/jcm11082147 10.1016/j.arth.2010.04.008 10.1029/2021MS002881 10.1155/2015/979560 10.3389/fmed.2017.00085 10.1007/s10916-022-01798-z 10.1007/978-3-031-04083-2_2 10.7189/jogh.08.020303 10.1016/j.arth.2022.04.003 10.1186/s12874-020-01046-3 10.1007/s00402-022-04588-x 10.1186/s12911-022-01751-7 10.1186/s13063-020-04252-y 10.5194/gmd-15-5481-2022 10.1302/2046-3758.129.BJR-2023-0070.R2 10.1145/2939672.2939785 10.1186/s12891-017-1915-4 10.1016/j.ijmedinf.2021.104670 10.1016/j.ibmed.2023.100111 10.1016/j.arth.2016.05.038 |
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| Keywords | Patient-reported outcome measures Machine learning Duration of surgery |
| Language | English |
| License | 2025. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
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| References_xml | – volume: 8 start-page: 268 year: 2021 ident: 2927_CR11 publication-title: Arthroplasty Today doi: 10.1016/j.artd.2021.01.006 – volume: 136 start-page: 105405 year: 2020 ident: 2927_CR30 publication-title: Accid Anal Prev doi: 10.1016/j.aap.2019.105405 – volume: 16 start-page: 74 year: 2019 ident: 2927_CR4 publication-title: EClinicalMedicine doi: 10.1016/j.eclinm.2019.09.015 – ident: 2927_CR26 doi: 10.1214/aos/1013203451 – ident: 2927_CR18 doi: 10.1007/s00167-022-06957-w – volume: 17 start-page: 182 year: 2016 ident: 2927_CR1 publication-title: BMC Musculoskelet Disord doi: 10.1186/s12891-016-1025-8 – volume: 112 start-page: 41 year: 2010 ident: 2927_CR35 publication-title: Anesthesiology doi: 10.1097/ALN.0b013e3181c294c2 – volume: 4 start-page: 1 year: 2021 ident: 2927_CR12 publication-title: J Bus Analytics doi: 10.1080/2573234X.2021.1873078 – volume: 101–B start-page: 51 year: 2019 ident: 2927_CR2 publication-title: bone Joint J doi: 10.1302/0301-620X.101B6.BJJ-2018-1400.R1 – volume: 17 start-page: e0273831 year: 2022 ident: 2927_CR32 publication-title: PLoS ONE doi: 10.1371/journal.pone.0273831 – volume-title: The elements of statistical learning: data mining, inference, and prediction year: 2017 ident: 2927_CR21 – ident: 2927_CR27 doi: 10.1016/B978-0-12-809633-8.20349-X – ident: 2927_CR34 doi: 10.3390/bdcc6030076 – volume: 71 start-page: E39 year: 2022 ident: 2927_CR13 publication-title: Nurs Res doi: 10.1097/NNR.0000000000000602 – ident: 2927_CR17 doi: 10.3390/jcm11082147 – volume: 25 start-page: 49 year: 2010 ident: 2927_CR6 publication-title: J Arthroplast doi: 10.1016/j.arth.2010.04.008 – ident: 2927_CR36 doi: 10.1029/2021MS002881 – ident: 2927_CR3 doi: 10.1155/2015/979560 – volume: 4 start-page: 85 year: 2017 ident: 2927_CR20 publication-title: Front Med doi: 10.3389/fmed.2017.00085 – volume: 46 start-page: 19 year: 2022 ident: 2927_CR25 publication-title: J Med Syst doi: 10.1007/s10916-022-01798-z – start-page: 13 volume-title: xxAI - beyond explainable AI year: 2022 ident: 2927_CR31 doi: 10.1007/978-3-031-04083-2_2 – ident: 2927_CR15 doi: 10.7189/jogh.08.020303 – ident: 2927_CR10 doi: 10.1016/j.arth.2022.04.003 – volume: 20 start-page: 171 year: 2020 ident: 2927_CR16 publication-title: BMC Med Res Methodol doi: 10.1186/s12874-020-01046-3 – ident: 2927_CR19 doi: 10.1007/s00402-022-04588-x – volume: 22 start-page: 18 year: 2022 ident: 2927_CR7 publication-title: BMC Med Inf Decis Mak doi: 10.1186/s12911-022-01751-7 – volume: 21 start-page: 322 year: 2020 ident: 2927_CR23 publication-title: Trials doi: 10.1186/s13063-020-04252-y – volume: 15 start-page: 5481 year: 2022 ident: 2927_CR29 publication-title: Geosci Model Dev doi: 10.5194/gmd-15-5481-2022 – volume: 12 start-page: 512 year: 2023 ident: 2927_CR8 publication-title: Bone Joint Res doi: 10.1302/2046-3758.129.BJR-2023-0070.R2 – ident: 2927_CR22 doi: 10.1145/2939672.2939785 – volume: 18 start-page: 544 year: 2017 ident: 2927_CR5 publication-title: BMC Musculoskelet Disord doi: 10.1186/s12891-017-1915-4 – volume: 158 start-page: 104670 year: 2022 ident: 2927_CR14 publication-title: Int J Med Informatics doi: 10.1016/j.ijmedinf.2021.104670 – volume-title: Congress on Evolutionary Computation year: 2021 ident: 2927_CR28 – volume: 8 start-page: 100111 year: 2023 ident: 2927_CR9 publication-title: Intelligence-Based Med doi: 10.1016/j.ibmed.2023.100111 – volume: 28 start-page: 112 year: 2012 ident: 2927_CR24 publication-title: Bioinf (Oxford England) – volume: 31 start-page: 2768 year: 2016 ident: 2927_CR33 publication-title: J Arthroplast doi: 10.1016/j.arth.2016.05.038 |
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Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We... Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore... Background Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We... BackgroundDuration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We... Machine learning predicts surgery duration for hip and knee arthroplasty with a mean error of slightly above 10 min. Performance was comparable for a... Abstract Background Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse... |
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| SubjectTerms | Aged Algorithms Arthroplasty (hip) Arthroplasty (knee) Arthroplasty, Replacement, Hip - statistics & numerical data Arthroplasty, Replacement, Knee - statistics & numerical data Artificial intelligence Body mass index Body size Clinical trials Datasets Duration of surgery Female Health aspects Health Informatics Hip Hospitals Humans Infections Information Systems and Communication Service Joint replacement surgery Knee Learning algorithms Machine Learning Male Management of Computing and Information Systems Medicine Medicine & Public Health Middle Aged Missing data Patient safety Patient-reported outcome measures Patients Regression Regression analysis Retrospective Studies Risk factors Surgeons Surgery Time Factors Validation studies Variables |
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| Title | Leveraging machine learning for duration of surgery prediction in knee and hip arthroplasty – a development and validation study |
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