Natural language processing with machine learning to predict outcomes after ovarian cancer surgery.

Journal: Gynecologic oncology
Published Date: Oct 14, 2020

Abstract

OBJECTIVE: To determine if natural language processing (NLP) with machine learning of unstructured full text documents (a preoperative CT scan) improves the ability to predict postoperative complication and hospital readmission among women with ovarian cancer undergoing surgery when compared with discrete data predictors alone.

Authors

  • Emma L Barber
    Northwestern University Feinberg School of Medicine, Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Chicago, IL, USA; Robert H Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, United States of America; Center for Health Equity Transformation, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America; Institute of Public Health and Medicine Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America. Electronic address: emma.barber@northwestern.edu.
  • Ravi Garg
    Center for Healthcare Studies, Northwestern University, Chicago, Illinois.
  • Christianne Persenaire
    Northwestern University Feinberg School of Medicine, Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Chicago, IL, USA.
  • Melissa Simon
    Northwestern University Feinberg School of Medicine, Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Chicago, IL, USA; Robert H Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, United States of America; Center for Health Equity Transformation, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America; Institute of Public Health and Medicine Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America.

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