Development and Internal Validation of Machine Learning Algorithms for Preoperative Survival Prediction of Extremity Metastatic Disease.

Journal: Clinical orthopaedics and related research
PMID: 31651589

Abstract

BACKGROUND: A preoperative estimation of survival is critical for deciding on the operative management of metastatic bone disease of the extremities. Several tools have been developed for this purpose, but there is room for improvement. Machine learning is an increasingly popular and flexible method of prediction model building based on a data set. It raises some skepticism, however, because of the complex structure of these models.

Authors

  • Quirina C B S Thio
    Q. C. B. S. Thio, A. V. Karhade, P. T. Ogink, K. Raskin, S. Lozano-Calderon, J. H. Schwab, Division of Orthopaedic Oncology, Department of Orthopaedics, Massachusetts General Hospital-Harvard Medical School, Boston, MA, USA K. de Amorim Bernstein, Department of Radiation Oncology, Massachusetts General Hospital-Harvard Medical School, Boston, MA, USA.
  • Aditya V Karhade
    Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Bas JJ Bindels
    Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Paul T Ogink
  • Jos A M Bramer
    Department of Orthopedic Surgery, Academic University Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
  • Marco L Ferrone
    Department of Orthopaedic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
  • Santiago Lozano Calderón
    Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Kevin A Raskin
  • Joseph H Schwab
    Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. Electronic address: jhschwab@mgh.harvard.edu.

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