Identification of colorectal cancer using structured and free text clinical data.

Journal: Health informatics journal
Published Date: Jan 1, 2022

Abstract

Colorectal cancer incidence has continually fallen among those 50 years old and over. However, the incidence has increased in those under 50. Even with the recent screening guidelines recommending that screening begins at age 45, nearly half of all early-onset colorectal cancer will be missed. Methods are needed to identify high-risk individuals in this age group for targeted screening. Colorectal cancer studies, as with other clinical studies, have required labor intensive chart review for the identification of those affected and risk factors. Natural language processing and machine learning can be used to automate the process and enable the screening of large numbers of patients. This study developed and compared four machine learning and statistical models: logistic regression, support vector machine, random forest, and deep neural network, in their performance in classifying colorectal cancer patients. Excellent classification performance is achieved with AUCs over 97%.

Authors

  • Douglas F Redd
    19986Washington DC VA Medical Center, Washington, DC, USA Biomedical Informatics Center, 43989The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
  • Yijun Shao
    Veterans Affairs Medical Center, Washington, DC; George Washington University, Washington, DC.
  • Qing Zeng-Treitler
    Veterans Affairs Medical Center, Washington, DC; George Washington University, Washington, DC.
  • Laura J Myers
    20015Richard L Roudebush VA Medical Center, Indianapolis, IN, USAIndiana University School of Medicine, Indianapolis, IN, USA Regenstrief Institute Inc, Indianapolis, IN, USA.
  • Barry C Barker
    20015Richard L Roudebush VA Medical Center, Indianapolis, IN, USA.
  • Stuart J Nelson
    George Washington University, Washington, DC, DC, United States.
  • Thomas F Imperiale
    1] Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana, USA [2] Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA [3] Center of Innovation, Health Services Research and Development, Richard L, Roudebush VA Medical Center, Indianapolis, Indiana, USA [4] Health Services Research, Regenstrief Institute, Indianapolis, Indiana, USA.

Keywords

External Resources

Popular Topics
Recent Journals