Automated model versus treating physician for predicting survival time of patients with metastatic cancer.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 33313792

Abstract

OBJECTIVE: Being able to predict a patient's life expectancy can help doctors and patients prioritize treatments and supportive care. For predicting life expectancy, physicians have been shown to outperform traditional models that use only a few predictor variables. It is possible that a machine learning model that uses many predictor variables and diverse data sources from the electronic medical record can improve on physicians' performance. For patients with metastatic cancer, we compared accuracy of life expectancy predictions by the treating physician, a machine learning model, and a traditional model.

Authors

  • Michael F Gensheimer
    Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
  • Sonya Aggarwal
    Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
  • Kathryn R K Benson
    Department of Radiation Oncology, Stanford University, Stanford, CA, USA.
  • Justin N Carter
    Department of Radiation Oncology, Stanford University, Stanford, CA, USA.
  • A Solomon Henry
    Research Informatics Center, Stanford University, Stanford, CA.
  • Douglas J Wood
    Research Informatics Center, Stanford University, Stanford, CA.
  • Scott G Soltys
  • Steven Hancock
    2 Department of Radiation Oncology, Stanford University, Stanford, CA, USA.
  • Erqi Pollom
    Department of Radiation Oncology, Stanford University, Stanford, CA, USA.
  • Nigam H Shah
    Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
  • Daniel T Chang
    Department of Radiation Oncology, School of Medicine, Stanford University, Stanford, California 94305.

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