The Relative Importance of Clinical and Socio-demographic Variables in Prognostic Prediction in Non-Small Cell Lung Cancer: A Variable Importance Approach.

Journal: Medical care
PMID: 31985586

Abstract

BACKGROUND: Prognostic modeling in health care has been predominantly statistical, despite a rapid growth of literature on machine-learning approaches in biological data analysis. We aim to assess the relative importance of variables in predicting overall survival among patients with non-small cell lung cancer using a Variable Importance (VIMP) approach in a machine-learning Random Survival Forest (RSF) model for posttreatment planning and follow-up.

Authors

  • Jiangping He
    School of Science and Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China.
  • James X Zhang
    Departments of Medicine.
  • Chin-Tu Chen
    Radiology, The University of Chicago, Chicago, IL.
  • Yan Ma
    Medical School of Chinese PLA, 100853 Beijing, China.
  • Raymond De Guzman
    Radiology, The University of Chicago, Chicago, IL.
  • Jianfeng Meng
    Department of Respiratory and Critical Care Medicine, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, China.
  • Yonglin Pu
    Radiology, The University of Chicago, Chicago, IL.

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