title: Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.
abstract: 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.
  
  
  
  To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence.
  
  
  
  A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method.
  
  
  
  The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models.
  
  
  
  A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community.
authors.affiliation: Centre for Pattern Recognition and Data Analytics, School of Information Technology, Deakin University, Geelong, Australia.
authors.forename: Wei
authors.identifier: 0000-0002-4711-7543
authors.initials: W
authors.lastname: Luo
authors.affiliation: Deakin University, Geelong, Australia.
authors.forename: Dinh
authors.identifier: 0000-0002-9977-8247
authors.initials: D
authors.lastname: Phung
authors.affiliation: Deakin University, Geelong, Australia.
authors.forename: Truyen
authors.identifier: 0000-0001-6531-8907
authors.initials: T
authors.lastname: Tran
authors.affiliation: Deakin University, Geelong, Australia.
authors.forename: Sunil
authors.identifier: 0000-0002-3308-1930
authors.initials: S
authors.lastname: Gupta
authors.affiliation: Deakin University, Geelong, Australia.
authors.forename: Santu
authors.identifier: 0000-0003-2247-850X
authors.initials: S
authors.lastname: Rana
authors.affiliation: Deakin University, Geelong, Australia.
authors.forename: Chandan
authors.identifier: 0000-0003-1814-0856
authors.initials: C
authors.lastname: Karmakar
authors.affiliation: Deakin University, Geelong, Australia.
authors.forename: Alistair
authors.identifier: 0000-0002-0849-3271
authors.initials: A
authors.lastname: Shilton
authors.affiliation: Deakin University, Geelong, Australia.
authors.forename: John
authors.identifier: 0000-0002-7562-6767
authors.initials: J
authors.lastname: Yearwood
authors.affiliation: Philips Research, Briarcliff Manor, NY, United States.
authors.forename: Nevenka
authors.identifier: 0000-0003-4091-9233
authors.initials: N
authors.lastname: Dimitrova
authors.affiliation: Japan Advanced Institute of Science and Technology, Nomi, Japan.
authors.forename: Tu Bao
authors.identifier: 0000-0001-5951-643X
authors.initials: TB
authors.lastname: Ho
authors.affiliation: Deakin University, Geelong, Australia.
authors.forename: Svetha
authors.identifier: 0000-0001-8675-6631
authors.initials: S
authors.lastname: Venkatesh
authors.affiliation: Deakin University, Geelong, Australia.
authors.forename: Michael
authors.identifier: 0000-0002-5554-6946
authors.initials: M
authors.lastname: Berk
mesh_terms: D035843:Biomedical Research / Q000379:methods* / Q000592:standards; D003627:Data Interpretation, Statistical; D006801:Humans; D058025:Interdisciplinary Studies; D000069550:Machine Learning; D008954:Models, Biological
keywords: clinical prediction rule; guideline; machine learning