A machine learning approach to high‐risk cardiac surgery risk scoring-Reference-Cited by-同舟云学术

A machine learning approach to high‐risk cardiac surgery risk scoring

Published:2022-11-08 Issue:12 Volume:37 Page:4612-4620
ISSN:0886-0440
Container-title:Journal of Cardiac Surgery
language:en
Short-container-title:Journal of Cardiac Surgery

Author:

Rogers Michael P.¹^ORCID,Janjua Haroon¹,Fishberger Gregory¹,Harish Abhinav¹,Sujka Joseph¹,Toloza Eric M.²,DeSantis Anthony J.¹,Hooker Robert L.³,Pietrobon Ricardo⁴,Lozonschi Lucian⁵,Kuo Paul C.¹

Affiliation:

1. Department of Surgery University of South Florida Morsani College of Medicine Tampa Florida USA

2. Department of Oncologic Sciences University of South Florida Morsani College of Medicine Tampa Florida USA

3. Department of Surgery, Division of Cardiothoracic Surgery University of Arizona Tuscon Arizona USA

4. SporeData Inc. Durham North Carolina USA

5. Division of Cardiothoracic Surgery and Transplantation University of South Florida Morsani College of Medicine Tampa Florida USA

Publisher

Hindawi Limited

Subject

Cardiology and Cardiovascular Medicine,Surgery,Pulmonary and Respiratory Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jocs.17110

Reference23 articles.

1. The Society of Thoracic Surgeons 2018 Adult Cardiac Surgery Risk Models: Part 1—Background, Design Considerations, and Model Development

2. The NSQIP: A new frontier in surgery

3. Machine learning models for mitral valve replacement: A comparative analysis with the Society of Thoracic Surgeons risk score

4. Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations

5. Surgical Risk Is Not Linear: Derivation and Validation of a Novel, User-friendly, and Machine-learning-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator

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