Probabilistic forecasting of surgical case duration using machine learning: model development and validation.

Journal: Journal of the American Medical Informatics Association : JAMIA

Published Date: Dec 9, 2020

Abstract

OBJECTIVE: Accurate estimations of surgical case durations can lead to the cost-effective utilization of operating rooms. We developed a novel machine learning approach, using both structured and unstructured features as input, to predict a continuous probability distribution of surgical case durations.

Authors

York Jiao

Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, USA.
Anshuman Sharma

Department of Anesthesiology, Washington University in St Louis, St Louis, Missouri, USA.
Arbi Ben Abdallah

Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA.
Thomas M Maddox

From the VA Eastern Colorado Healthcare System, Cardiology Section, University of Colorado School of Medicine, Colorado Cardiovascular Outcomes Research (CCOR) Consortium, Denver (T.M.M.); and VA Tennessee Valley Healthcare System, Medicine Department, Department of Biomedical Informatics, Medicine, and Biostatistics, Vanderbilt University, Nashville (M.A.M.). thomas.maddox@va.gov.
Thomas Kannampallil

Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, USA.

Keywords

Datasets as Topic Forecasting Humans Machine Learning Models, Statistical Natural Language Processing Neural Networks, Computer Operating Rooms Operative Time Reproducibility of Results Surgical Procedures, Operative

External Resources

View on PubMed Access via DOI PubMed (33031543)

Probabilistic forecasting of surgical case duration using machine learning: model development and validation.

Abstract

Authors

Keywords

External Resources

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