Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.

Journal: Journal of medical Internet research
Published Date: Dec 16, 2016

Abstract

BACKGROUND: As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs.

Authors

  • Wei Luo
    Centre for Pattern Recognition and Data Analytics, School of Information Technology, Deakin University, Geelong, Victoria, Australia.
  • Dinh Phung
    Centre for Pattern Recognition and Data Analytics, School of Information Technology, Deakin University, Geelong, Victoria, Australia.
  • Truyen Tran
    Centre for Pattern Recognition and Data Analytics, School of Information Technology, Deakin University, Geelong, Victoria, Australia.
  • Sunil Gupta
    Applied Artificial Intelligence Institute, Deakin University, Melbourne, Australia.
  • Santu Rana
    Centre for Pattern Recognition and Data Analytics, School of Information Technology, Deakin University, Geelong, Victoria, Australia.
  • Chandan Karmakar
    Deakin University, Geelong, Australia.
  • Alistair Shilton
    Deakin University, Geelong, Australia.
  • John Yearwood
    Deakin University, Geelong, Australia.
  • Nevenka Dimitrova
    Philips Research, Briarcliff Manor, NY, United States.
  • Tu Bao Ho
    Japan Advanced Institute of Science and Technology, Nomi, Japan.
  • Svetha Venkatesh
    Applied Artificial Intelligence Institute, Deakin University, Melbourne, Australia.
  • Michael Berk
    IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.

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