Drivers of Prolonged Hospitalization Following Spine Surgery: A Game-Theory-Based Approach to Explaining Machine Learning Models.

Journal: The Journal of bone and joint surgery. American volume

PMID: 33186002

Abstract

BACKGROUND: Understanding the interactions between variables that predict prolonged hospital length of stay (LOS) following spine surgery can help uncover drivers of this risk in patients. This study utilized a novel game-theory-based approach to develop explainable machine learning models to understand such interactions in a large cohort of patients treated with spine surgery.

Authors

Michael L Martini

Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Sean N Neifert

Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York.
Jonathan S Gal
Eric K Oermann

Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Jeffrey T Gilligan
John M Caridi

Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY.

Keywords

Comorbidity Computer Simulation Critical Care Elective Surgical Procedures Female Game Theory Humans Intensive Care Units Length of Stay Machine Learning Male Medicaid Middle Aged Nervous System Diseases Risk Factors Spinal Diseases United States

External Resources

View on PubMed Access via DOI PubMed (33186002)

Drivers of Prolonged Hospitalization Following Spine Surgery: A Game-Theory-Based Approach to Explaining Machine Learning Models.

Abstract

Authors

Keywords

External Resources

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