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Using Machine Learning (XGBoost) to Predict Outcomes followi… : Annals of Surgery


*Department of Surgery, University of Toronto, Canada

Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, Canada

Institute of Medical Science, University of Toronto, Canada

§Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Canada

Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Canada

Data Science & Advanced Analytics, Unity Health Toronto, University of Toronto, Canada

#Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Canada

**Institute of Health Policy, Management and Evaluation, University of Toronto, Canada

††ICES, University of Toronto, Canada

‡‡Department of Surgery, King Saud University, Kingdom of Saudi Arabia

§§School of Medicine, Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Ireland

∥∥Department of Anesthesia, St. Michael’s Hospital, Unity Health Toronto, Canada

¶¶Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Unity Health Toronto, Canada

##Division of General Surgery, St. Michael’s Hospital, Unity Health Toronto, Canada

***Leslie Dan Faculty of Pharmacy, University of Toronto, Canada

†††Department of Surgery, King Faisal Specialist Hospital and Research Center, Kingdom of Saudi Arabia

Funding: This research was partially funded by the Canadian Institutes of Health Research, Ontario Ministry of Health, and PSI Foundation (BL). The funding sources did not play a role in the design or conduct of the research.

AUTHOR CONTRIBUTIONS: BL: concept, design, acquisition of data, analysis and interpretation of data, drafting the article, final approval of article. NB (database manager): acquisition, analysis, and interpretation of data, revising article critically for important intellectual content, final approval of article. DB (data scientist): analysis and interpretation of data, support for model development, revising article critically for important intellectual content, final approval of article. DSL: analysis and interpretation of data, revising article critically for important intellectual content, final approval of article. BA: analysis and interpretation of data, revising article critically for important intellectual content, final approval of article. RV: analysis and interpretation of data, revising article critically for important intellectual content, final approval of article. DNW: analysis and interpretation of data, revising article critically for important intellectual content, final approval of article. ODR: analysis and interpretation of data, revising article critically for important intellectual content, final approval of article. CdeM: concept, design, analysis and interpretation of data, revising article critically for important intellectual content, final approval of article. MM: concept, design, analysis and interpretation of data, revising article critically for important intellectual content, final approval of article. GRN: concept, design, analysis and interpretation of data, revising article critically for important intellectual content, final approval of article. MAO: concept, design, acquisition of data, analysis and interpretation of data, revising article critically for important intellectual content, final approval of article

SOURCES OF FUNDING: This research was partially funded by the Canadian Institutes of Health Research, Ontario Ministry of Health, and PSI Foundation (BL). The funding sources did not play a role in the design or conduct of the research.

CODE AVAILABILITY STATEMENT: The complete code used for model development and evaluation in this project is publicly available on GitHub: https://github.com/benli12345/INFRA-ML-VQI.

DATA AVAILABILITY STATEMENT: The data used for this study comes from the Vascular Quality Initiative Database, which is maintained by the Society for Vascular Surgery Patient Safety Organization. Access and use of the data requires approval through an application process available at https://www.vqi.org/data-analysis/.

DISCLOSURES: The authors have no conflicts of interest.

[email protected]

Supplemental Digital Content is available for this article. Direct URL citations are provided in the HTML and PDF versions of this article on the journal’s website, www.annalsofsurgery.com.



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