Machine Learning Offers Exciting Potential for Predicting Postprocedural Outcomes: A Framework for Developing Random Forest Models in IR.

Journal: Journal of vascular and interventional radiology : JVIR
PMID: 32376173

Abstract

PURPOSE: To demonstrate that random forest models trained on a large national sample can accurately predict relevant outcomes and may ultimately contribute to future clinical decision support tools in IR.

Authors

  • Ishan Sinha
    Warren Alpert Medical School of Brown University, Providence, Rhode Island; Brown Center for Biomedical Informatics, Brown University, 233 Richmond Street, Box G-R, Providence, RI 02912. Electronic address: ishan_sinha@brown.edu.
  • Dilum P Aluthge
    Warren Alpert Medical School of Brown University, Providence, Rhode Island; Brown Center for Biomedical Informatics, Brown University, 233 Richmond Street, Box G-R, Providence, RI 02912.
  • Elizabeth S Chen
    Center for Clinical & Translational Science, University of Vermont, Burlington, VT; Department of Medicine, University of Vermont, Burlington, VT.
  • Indra Neil Sarkar
    Center for Clinical & Translational Science, University of Vermont, Burlington, VT; Department of Microbiology & Molecular Genetics, University of Vermont, Burlington, VT.
  • Sun Ho Ahn
    Division of Interventional Radiology, Department of Diagnostic Imaging, Providence, Rhode Island.

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