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. Attached Files Name Description MIMEType Size Downloads wei-guidelinesfordeveloping-2016.pdf Published version application/pdf 865.48KB 277 Title Guidelines for developing and reporting machine learning predictive models in biomedical research : a multidisciplinary view Author(s) Luo, Wei orcid.org/0000-0002-4711-7543 Phung, Quoc-Dinh orcid.org/0000-0002-9977-8247 Tran, Truyen orcid.org/0000-0001-6531-8907 Gupta, Sunil orcid.org/0000-0002-3308-1930 Rana, Santu orcid.org/0000-0003-2247-850X Karmakar, Chandan orcid.org/0000-0003-1814-0856 Shilton, Alistair orcid.org/0000-0002-7562-6767 Yearwood, John Leighton Dimitrova, N Ho, TB orcid.org/0000-0001-8675-6631 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 Persistent URL http://hdl.handle.net/10536/DRO/DU:30090315 Document type: Journal Article Collections: Faculty of Health School of Medicine Faculty of Science, Engineering and Built Environment School of Information Technology Centre for Pattern Recognition and Data Analytics (PRADA) Open Access Collection Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Wed, 21 Dec 2016, 09:29:55 EST Wed, 21 Dec 2016, 09:30:47 EST Thu, 22 Dec 2016, 04:00:24 EST Thu, 22 Dec 2016, 05:06:50 EST Mon, 09 Jan 2017, 12:48:53 EST Mon, 09 Jan 2017, 12:51:03 EST Sat, 21 Jan 2017, 01:32:49 EST Sat, 04 Feb 2017, 01:30:17 EST Thu, 09 Feb 2017, 11:04:11 EST Thu, 09 Feb 2017, 11:04:47 EST Fri, 07 Sep 2018, 12:20:39 EST Filtered Full Citation counts:   Cited 175 times in TR Web of Science Cited 180 times in Scopus Search Google Scholar Access Statistics: 670 Abstract Views, 278 File Downloads  -  Detailed Statistics Created: Wed, 21 Dec 2016, 09:30:47 EST

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.