This paper is in the following e-collection/theme issue:

Artificial Intelligence in Surgery and Perioperative Medicine (34) Machine Learning (1719) Perioperative Software Tools for Clinicians (19)

Published on in Vol 6 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39650, first published .
An Ensemble Learning Approach to Improving Prediction of Case Duration for Spine Surgery: Algorithm Development and Validation

An Ensemble Learning Approach to Improving Prediction of Case Duration for Spine Surgery: Algorithm Development and Validation

An Ensemble Learning Approach to Improving Prediction of Case Duration for Spine Surgery: Algorithm Development and Validation

Authors of this article:

Rodney Allanigue Gabriel1, 2 Author Orcid Image ;   Bhavya Harjai2 Author Orcid Image ;   Sierra Simpson2 Author Orcid Image ;   Austin Liu Du3 Author Orcid Image ;   Jeffrey Logan Tully2 Author Orcid Image ;   Olivier George4 Author Orcid Image ;   Ruth Waterman5 Author Orcid Image
Article Authors Cited by (11) Tweetations (4) Metrics

Journals

  1. Adams M, Nelson A, Narouze S. Daring discourse: artificial intelligence in pain medicine, opportunities and challenges. Regional Anesthesia & Pain Medicine 2023;48(9):439 View
  2. Arina P, Kaczorek M, Hofmaenner D, Pisciotta W, Refinetti P, Singer M, Mazomenos E, Whittle J. Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools. Anesthesiology 2024;140(1):85 View
  3. Zeleke A, Palumbo P, Tubertini P, Miglio R, Chiari L. Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis. Frontiers in Artificial Intelligence 2023;6 View
  4. Spence C, Shah O, Cebula A, Tucker K, Sochart D, Kader D, Asopa V. Machine learning models to predict surgical case duration compared to current industry standards: scoping review. BJS Open 2023;7(6) View
  5. Lippenberger F, Ziegelmayer S, Berlet M, Feussner H, Makowski M, Neumann P, Graf M, Kaissis G, Wilhelm D, Braren R, Reischl S. Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections. International Journal of Colorectal Disease 2024;39(1) View
  6. Bellini V, Russo M, Domenichetti T, Panizzi M, Allai S, Bignami E. Artificial Intelligence in Operating Room Management. Journal of Medical Systems 2024;48(1) View
  7. Zeleke A, Palumbo P, Tubertini P, Miglio R, Chiari L. Comparison of nine machine learning regression models in predicting hospital length of stay for patients admitted to a general medicine department. Informatics in Medicine Unlocked 2024;47:101499 View
  8. Park J, Roh G, Kim K, Kim H. Development of Predictive Model of Surgical Case Durations Using Machine Learning Approach. Journal of Medical Systems 2025;49(1) View
  9. Abdelrazig Merghani A, Ahmed Esmail A, Mubarak Osman A, Abdelfrag Mohamed N, Mohamed Ali Shentour S, Abdelrazig Merghani S. The Role of Machine Learning in Management of Operating Room: A Systematic Review. Cureus 2025 View
  10. Chamarthi B, Polu O, Anumula S, Ushmani A, Kasralikar P, Aleem Syed A. Natural Language Processing (NLP)- and Machine Learning (ML)-Enabled Operating Room Optimization: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Systematic Review Anchored in Project Planning Theory. Cureus 2025 View

Conference Proceedings

  1. Li X, Zhan Y, Ni J, Cai F, Jin H, Lin X, Zhang Y, Tao D. Proceedings of the 2024 16th International Conference on Bioinformatics and Biomedical Technology. Multimodal Feature Analysis for Surgery Duration Predication View