[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123830-en":3,"doc-seo-123830-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123830,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",7,"Healthcare","Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database - Algorithm Development and Validation Study","Accurate predictions of procedural case durations are essential for perioperative staffing, operating room resource allocation, and patient communication. This study evaluates whether a machine learning algorithm scalable across multiple centers can estimate procedure duration within a tolerance threshold. Models including deep learning, gradient boosting, and ensembles were trained on perioperative data at three time points and validated against historical-mean baseline performance. Gradient boosting achieved the lowest mean absolute error and improved prediction accuracy while using explainability methods.","UCSF  \nUC San Francisco Previously Published Works  \nTitle  \nMachine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation Study.  \nPermalink  \n[https://escholarship.org/uc/item/659154f1](https://escholarship.org/uc/item/659154f1)  \nAuthors  \nKendale, Samir  \nBishara, Andrew Burns, Michael et al.  \nPublication Date  \n2023-09-08  \nDOI  \n10.2196/44909  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nJMIR AI Kendale et al  \nOriginal Paper  \nMachine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation Study  \n\n| Samir Kendale1*, MD; Andrew Bishara2,3*, MD; Michael Burns4*, MD, PhD; Stuart Solomon5, MD; Matthew Corriere6*, MD; Michael Mathis4,7*, MD |\n| --- |\n| 1Department of Anesthesia, Critical Care & Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States 2Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA, United States 3Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, United States 4Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States\u003Cbr>5Department of Anesthesiology, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States 6Department of Surgery, Section of Vascular Surgery, University of Michigan Medical School, Ann Arbor, MI, United States 7Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, United States *these authors contributed equally\u003Cbr>Corresponding Author:\u003Cbr>Samir Kendale, MD\u003Cbr>Department of Anesthesia, Critical Care & Pain Medicine Beth Israel Deaconess Medical Center\u003Cbr>1 Deaconess Road Boston, MA, 02215 United States Phone: 1 6177545400\u003Cbr>Email: [skendale@bidmc.harvard.edu](skendale@bidmc.harvard.edu)\u003Cbr>Abstract |\n\nBackground: Accurate projections of procedural case durations are complex but critical to the planning of perioperative staffing, operating room resources, and patient communication. Nonlinear prediction models using machine learning methods may provide opportunities for hospitals to improve upon current estimates of procedure duration.  \nObjective: The aim of this study was to determine whether a machine learning algorithm scalable across multiple centers could make estimations of case duration within a tolerance limit because there are substantial resources required for operating room functioning that relate to case duration.  \nMethods: Deep learning, gradient boosting, and ensemble machine learning models were generated using perioperative data available at 3 distinct time points: the time of scheduling, the time of patient arrival to the operating or procedure room (primary model), and the time of surgical incision or procedure start. The primary outcome was procedure duration, defined by the time between the arrival and the departure of the patient from the procedure room. Model performance was assessed by mean absolute error (MAE), the proportion of predictions falling within 20% of the actual duration, and other standard metrics. Performance was compared with a baseline method of historical means within a linear regression model. Model features driving predictions were assessed using Shapley additive explanations values and permutation feature importance.  \nResults: A total of 1,177,893 procedures from 13 academic and private hospitals between 2016 and 2019 were used. Across all procedures, the median procedure duration was 94 (IQR 50-167) minutes. In estimating ","cbCairE0XENYigAD","https://ap.wps.com/l/cbCairE0XENYigAD","pdf",888196,1,17,"English","en",105,"# Background\n## Study objective\n# Methods\n## Data sources and time points\n## Outcome and evaluation\n## Feature importance approaches\n# Results\n## Dataset and baseline comparison\n## Model performance metrics\n## Key predictive features\n# Conclusions","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study targets the difficulty of accurately projecting procedural case durations, which affects perioperative staffing, operating room resources, and patient communication.\"},{\"question\":\"Which machine learning models were evaluated?\",\"answer\":\"Deep learning, gradient boosting, and ensemble machine learning models were trained using perioperative data from three time points.\"},{\"question\":\"How was model performance measured and compared?\",\"answer\":\"Performance was assessed with mean absolute error (MAE) and the proportion of predictions within 20% of actual duration, and compared with a baseline historical-mean approach within a linear regression model.\"}]","Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database - 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