Predicting respiratory failure after pulmonary lobectomy using machine learning techniques.

Journal: Surgery
Published Date: Jul 15, 2020

Abstract

BACKGROUND: When pulmonary complications occur, postlobectomy patients have a higher mortality rate, increased length of stay, and higher readmission rates. Because of a lack of high-quality consolidated clinical data, it is challenging to assess and recognize at-risk thoracic patients to avoid respiratory failure and standardize outcome measures.

Authors

  • Siavash Bolourani
    Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY, USA.
  • Ping Wang
    School of Chemistry and Chemical Engineering, Shandong University of Technology, 255049, Zibo, PR China. Electronic address: wangping876@163.com.
  • Vihas M Patel
    Department of Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New Hyde Park, NY.
  • Frank Manetta
    Department of Cardiovascular and Thoracic Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY.
  • Paul C Lee
    Department of Cardiovascular and Thoracic Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY. Electronic address: plee15@northwell.edu.

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