Machine Learning Models for Prediction of Joint Infections Following Hip Replacement Surgery

Hilal Maradit Kremers; Sunghwan Sohn; Walter K. Kremers

doi:10.1109/BIBM58861.2023.10385421

Machine Learning Models for Prediction of Joint Infections Following Hip Replacement Surgery

Hilal Maradit Kremers, Sunghwan Sohn, Walter K. Kremers

Quantitative Health Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Periprosthetic joint infection (PJI) is a rare but serious complication following total hip replacement surgery. Personalized risk prediction and risk factor management can allow effective presurgical interventions and improved surgical outcomes. In this study, we implemented a data driven approach to develop PJI risk prediction models using large scale data from the electronic health records (EHR) at a large tertiary care hospital. Dataset comprised a total of 22,350 hip replacement surgeries with 283 (1.3%) PJI events within the 1-year window following surgery. We implemented four different models (classic lasso, relaxed lasso, gradient boosting model (GBM) and neural networks) and used 10-fold cross-validation to calculate measures of model performance. The relaxed lasso model using the Cox model structure outperformed the other models with a concordance of 0.793. Our analysis indicates large scale EHR data and machine learning models provide increased accuracy in prediction of joint infections in hip replacement patients.

Original language	English (US)
Title of host publication	Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Editors	Xingpeng Jiang, Haiying Wang, Reda Alhajj, Xiaohua Hu, Felix Engel, Mufti Mahmud, Nadia Pisanti, Xuefeng Cui, Hong Song
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3053-3058
Number of pages	6
ISBN (Electronic)	9798350337488
DOIs	https://doi.org/10.1109/BIBM58861.2023.10385421
State	Published - 2023
Event	2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 - Istanbul, Turkey Duration: Dec 5 2023 → Dec 8 2023

Publication series

Name	Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Conference

Conference	2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Country/Territory	Turkey
City	Istanbul
Period	12/5/23 → 12/8/23

Keywords

hip arthroplasty
infection
machine learning
prediction
relaxed lasso

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Automotive Engineering
Modeling and Simulation
Health Informatics

Access to Document

10.1109/BIBM58861.2023.10385421

Cite this

Kremers, H. M., Sohn, S., & Kremers, W. K. (2023). Machine Learning Models for Prediction of Joint Infections Following Hip Replacement Surgery. In X. Jiang, H. Wang, R. Alhajj, X. Hu, F. Engel, M. Mahmud, N. Pisanti, X. Cui, & H. Song (Eds.), Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 (pp. 3053-3058). (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM58861.2023.10385421

Machine Learning Models for Prediction of Joint Infections Following Hip Replacement Surgery. / Kremers, Hilal Maradit ; Sohn, Sunghwan ; Kremers, Walter K.
Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. ed. / Xingpeng Jiang; Haiying Wang; Reda Alhajj; Xiaohua Hu; Felix Engel; Mufti Mahmud; Nadia Pisanti; Xuefeng Cui; Hong Song. Institute of Electrical and Electronics Engineers Inc., 2023. p. 3053-3058 (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kremers, HM , Sohn, S & Kremers, WK 2023, Machine Learning Models for Prediction of Joint Infections Following Hip Replacement Surgery. in X Jiang, H Wang, R Alhajj, X Hu, F Engel, M Mahmud, N Pisanti, X Cui & H Song (eds), Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Institute of Electrical and Electronics Engineers Inc., pp. 3053-3058, 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Istanbul, Turkey, 12/5/23. https://doi.org/10.1109/BIBM58861.2023.10385421

Kremers HM , Sohn S , Kremers WK. Machine Learning Models for Prediction of Joint Infections Following Hip Replacement Surgery. In Jiang X, Wang H, Alhajj R, Hu X, Engel F, Mahmud M, Pisanti N, Cui X, Song H, editors, Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 3053-3058. (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023). doi: 10.1109/BIBM58861.2023.10385421

Kremers, Hilal Maradit ; Sohn, Sunghwan ; Kremers, Walter K. / Machine Learning Models for Prediction of Joint Infections Following Hip Replacement Surgery. Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. editor / Xingpeng Jiang ; Haiying Wang ; Reda Alhajj ; Xiaohua Hu ; Felix Engel ; Mufti Mahmud ; Nadia Pisanti ; Xuefeng Cui ; Hong Song. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 3053-3058 (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023).

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title = "Machine Learning Models for Prediction of Joint Infections Following Hip Replacement Surgery",

abstract = "Periprosthetic joint infection (PJI) is a rare but serious complication following total hip replacement surgery. Personalized risk prediction and risk factor management can allow effective presurgical interventions and improved surgical outcomes. In this study, we implemented a data driven approach to develop PJI risk prediction models using large scale data from the electronic health records (EHR) at a large tertiary care hospital. Dataset comprised a total of 22,350 hip replacement surgeries with 283 (1.3%) PJI events within the 1-year window following surgery. We implemented four different models (classic lasso, relaxed lasso, gradient boosting model (GBM) and neural networks) and used 10-fold cross-validation to calculate measures of model performance. The relaxed lasso model using the Cox model structure outperformed the other models with a concordance of 0.793. Our analysis indicates large scale EHR data and machine learning models provide increased accuracy in prediction of joint infections in hip replacement patients.",

keywords = "hip arthroplasty, infection, machine learning, prediction, relaxed lasso",

author = "Kremers, {Hilal Maradit} and Sunghwan Sohn and Kremers, {Walter K.}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 ; Conference date: 05-12-2023 Through 08-12-2023",

year = "2023",

doi = "10.1109/BIBM58861.2023.10385421",

language = "English (US)",

series = "Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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editor = "Xingpeng Jiang and Haiying Wang and Reda Alhajj and Xiaohua Hu and Felix Engel and Mufti Mahmud and Nadia Pisanti and Xuefeng Cui and Hong Song",

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AU - Sohn, Sunghwan

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N2 - Periprosthetic joint infection (PJI) is a rare but serious complication following total hip replacement surgery. Personalized risk prediction and risk factor management can allow effective presurgical interventions and improved surgical outcomes. In this study, we implemented a data driven approach to develop PJI risk prediction models using large scale data from the electronic health records (EHR) at a large tertiary care hospital. Dataset comprised a total of 22,350 hip replacement surgeries with 283 (1.3%) PJI events within the 1-year window following surgery. We implemented four different models (classic lasso, relaxed lasso, gradient boosting model (GBM) and neural networks) and used 10-fold cross-validation to calculate measures of model performance. The relaxed lasso model using the Cox model structure outperformed the other models with a concordance of 0.793. Our analysis indicates large scale EHR data and machine learning models provide increased accuracy in prediction of joint infections in hip replacement patients.

AB - Periprosthetic joint infection (PJI) is a rare but serious complication following total hip replacement surgery. Personalized risk prediction and risk factor management can allow effective presurgical interventions and improved surgical outcomes. In this study, we implemented a data driven approach to develop PJI risk prediction models using large scale data from the electronic health records (EHR) at a large tertiary care hospital. Dataset comprised a total of 22,350 hip replacement surgeries with 283 (1.3%) PJI events within the 1-year window following surgery. We implemented four different models (classic lasso, relaxed lasso, gradient boosting model (GBM) and neural networks) and used 10-fold cross-validation to calculate measures of model performance. The relaxed lasso model using the Cox model structure outperformed the other models with a concordance of 0.793. Our analysis indicates large scale EHR data and machine learning models provide increased accuracy in prediction of joint infections in hip replacement patients.

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KW - machine learning

KW - prediction

KW - relaxed lasso

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Machine Learning Models for Prediction of Joint Infections Following Hip Replacement Surgery

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