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Guidelines for developing and reporting machine learning predictive models in biomedical research : a multidisciplinary view

Luo, Wei, Phung, Quoc-Dinh, Tran, Truyen, Gupta, Sunil, Rana, Santu, Karmakar, Chandan, Shilton, Alistair, Yearwood, John Leighton, Dimitrova, N, Ho, TB, Venkatesh, Svetha and Berk, Michael 2016, Guidelines for developing and reporting machine learning predictive models in biomedical research : a multidisciplinary view, Journal of medical internet research, vol. 18, no. 12, pp. 1-10, doi: 10.2196/jmir.5870.

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Title Guidelines for developing and reporting machine learning predictive models in biomedical research : a multidisciplinary view
Author(s) Luo, WeiORCID iD for Luo, Wei orcid.org/0000-0002-4711-7543
Phung, Quoc-DinhORCID iD for Phung, Quoc-Dinh orcid.org/0000-0002-9977-8247
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Karmakar, ChandanORCID iD for Karmakar, Chandan orcid.org/0000-0003-1814-0856
Shilton, Alistair
Yearwood, John Leighton
Dimitrova, N
Ho, TBORCID iD for Ho, TB orcid.org/0000-0001-8675-6631
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0002-5554-6946
Berk, Michael
Journal name Journal of medical internet research
Volume number 18
Issue number 12
Start page 1
End page 10
Total pages 10
Publisher JMIR Publications
Place of publication Toronto, Ont.
Publication date 2016-12
ISSN 1439-4456
1438-8871
Keyword(s) clinical prediction rule
guideline
machine learning
Summary 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.

OBJECTIVE: 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.

METHODS: A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method.

RESULTS: 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.

CONCLUSIONS: 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.
Language eng
DOI 10.2196/jmir.5870
Field of Research 080109 Pattern Recognition and Data Mining
090304 Medical Devices
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30090315

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.