System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-Directed Clinical Evaluations During Radiation and Chemoradiation.

Journal: Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Published Date: Nov 1, 2020

Abstract

PURPOSE: Patients undergoing outpatient radiotherapy (RT) or chemoradiation (CRT) frequently require acute care (emergency department evaluation or hospitalization). Machine learning (ML) may guide interventions to reduce this risk. There are limited prospective studies investigating the clinical impact of ML in health care. The objective of this study was to determine whether ML can identify high-risk patients and direct mandatory twice-weekly clinical evaluation to reduce acute care visits during treatment.

Authors

  • Julian C Hong
    All Authors: Duke University, Durham, NC.
  • Neville C W Eclov
    Department of Radiation Oncology, Duke University, Durham, NC.
  • Nicole H Dalal
    Department of Medicine, University of California, San Francisco, San Francisco, CA.
  • Samantha M Thomas
    Department of Biostatistics and Bioinformatics, Duke University, Durham, NC.
  • Sarah J Stephens
    Department of Radiation Oncology, Duke University, Durham, NC.
  • Mary Malicki
    Department of Radiation Oncology, Duke University, Durham, NC.
  • Stacey Shields
    Department of Radiation Oncology, Duke University, Durham, NC.
  • Alyssa Cobb
    Department of Radiation Oncology, Duke University, Durham, NC.
  • Yvonne M Mowery
    Department of Radiation Oncology, Duke University, Durham, NC.
  • Donna Niedzwiecki
    All Authors: Duke University, Durham, NC.
  • Jessica D Tenenbaum
    All Authors: Duke University, Durham, NC.
  • Manisha Palta
    Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States.

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