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

Foundations of AI (47) Artificial Intelligence (1421) AI Data (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 (4) Tweetations (2) Metrics

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  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
  4. Meyers A, Daysalilar M, Dagal A, Wang M, Kutlu O, Akcin M. Quantifying the impact of surgical teams on each stage of the operating room process. Frontiers in Digital Health 2024;6 View