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

Artificial Intelligence (122) Artificial Intelligence (1216) Clinical Informatics (919) Clinical Information and Decision Making (1340) Tools, Programs and Algorithms (246) Machine Learning (1401) Decision Support for Health Professionals (1147) Development and Evaluation of Research Methods, Instruments and Tools (439) Perioperative Software Tools for Clinicians (13)

Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44909, first published .
Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation Study

Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation Study

Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation Study

Authors of this article:

Samir Kendale1 Author Orcid Image ;   Andrew Bishara2, 3 Author Orcid Image ;   Michael Burns4 Author Orcid Image ;   Stuart Solomon5 Author Orcid Image ;   Matthew Corriere6 Author Orcid Image ;   Michael Mathis4, 7 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

Journals

  1. Rohatgi N. JMIR Perioperative Medicine: A Global Journal for Publishing Interdisciplinary Innovations, Research, and Perspectives. JMIR Perioperative Medicine 2023;6:e54344 View
  2. Loukas C, Seimenis I, Prevezanou K, Schizas D. Prediction of remaining surgery duration in laparoscopic videos based on visual saliency and the transformer network. The International Journal of Medical Robotics and Computer Assisted Surgery 2024;20(2) View
  3. El Emam K, Leung T, Malin B, Klement W, Eysenbach G. Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS). Journal of Medical Internet Research 2024;26:e52508 View