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Published on 16.12.16 in Vol 18, No 12 (2016): December

This paper is in the following e-collection/theme issue:

    Original Paper

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

    1Centre for Pattern Recognition and Data Analytics, School of Information Technology, Deakin University, Geelong, Australia

    2Deakin University, Geelong, Australia

    3Philips Research, Briarcliff Manor, NY, United States

    4Japan Advanced Institute of Science and Technology, Nomi, Japan

    *all authors contributed equally

    Corresponding Author:

    Wei Luo, PhD

    Centre for Pattern Recognition and Data Analytics

    School of Information Technology

    Deakin University

    Building KA

    75 Pigdons Road

    Geelong, 3220


    Phone: 61 3 5227 3096

    Fax:61 3 5227 3096



    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.

    J Med Internet Res 2016;18(12):e323




    Big data is changing every industry. Medicine is no exception. With rapidly growing volume and diversity of data in health care and biomedical research, traditional statistical methods often are inadequate. By looking into other industries where modern machine learning techniques play central roles in dealing with big data, many health and biomedical researchers have started applying machine learning to extract valuable insights from ever-growing biomedical databases, in particular with predictive models [1,2]. The flexibility and prowess of machine learning models also enable us to leverage novel but extremely valuable sources of information, such as wearable device data and electronic health record data [3].

    Despite its popularity, it is difficult to find a universally agreed-upon definition for machine learning. Arguably, many machine learning methods can be traced back as far as 30 years ago [4]. However, machine learning started making a broad impact only in the last 10 years. The reviews by Jordan and Mitchell [5] and Ghahramani [6] provide accessible overviews for machine learning. In this paper, we focus on machine learning predictive methods and models. These include random forest, support vector machines, and other methods listed in Multimedia Appendix 1. They all share an important difference from the traditional statistical methods such as logistic regression or analysis of variance—the ability to make accurate predictions on unseen data. To optimize the prediction accuracy, often the methods do not attempt to produce interpretable models. This also allows them to handle a large number of variables common in most big data problems.

    Accompanying the flexibility of emerging machine learning techniques, however, is uncertainty and inconsistency in the use of such techniques. Machine learning, owing to its intrinsic mathematical and algorithmic complexity, is often considered a “black magic” that requires a delicate balance of a large number of conflicting factors. This, together with inadequate reporting of data sources and modeling process, makes research results reported in many biomedical papers difficult to interpret. It is not rare to see potentially spurious conclusions drawn from methodologically inadequate studies [7-11], which in turn compromises the credibility of other valid studies and discourages many researchers who could benefit from adopting machine learning techniques.

    Most pitfalls of applying machine learning techniques in biomedical research originate from a small number of common issues, including data leakage [12] and overfitting [13-15], which can be avoided by adopting a set of best practice standards. Recognizing the urgent need for such a standard, we created a minimum list of reporting items and a set of guidelines for optimal use of predictive models in biomedical research.


    Panel of Experts

    In 2015, a multidisciplinary panel was assembled to cover expertise in machine learning, traditional statistics, and biomedical applications of these methods. The candidate list was generated in two stages. The panel grew from a number of active machine learning researchers attending international conferences including the Asian Conference on Machine Learning, the Pacific Asia Conference on Knowledge Discovery and Data Mining, and the International Conference on Pattern Recognition. The responders were then asked to nominate additional researchers who apply machine learning in biomedical research. Effort was exercised to include researchers from different continents. Researchers from the list were approached through emails for joining the panel and/or recommending colleagues to be included. Two declined the invitation.

    The final panel included 11 researchers from 3 institutions on 3 different continents. Each panelist had experience and expertise in machine learning projects in biomedical applications and has learned from common pitfalls. The areas of research expertise included machine learning, data mining, computational intelligence, signal processing, information management, bioinformatics, and psychiatry. On average, each panel member had 8.5 years’ experience in either developing or applying machine learning methods. The diversity of the panel was reflected by the members’ affiliation with 3 different institutions across 3 continents.

    Development of Guidelines

    Using an iterative process, the panel 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. The Delphi method was used to generate the list of reporting items.

    The panelists were interviewed with multiple iterations of emails. Email 1 asked panelists to list topics to be covered in the guidelines. An aggregated topic list was generated. Email 2 asked each panelist to review the scope of the list and state his or her recommendation for each topic in the aggregated list. Later iterations of email interviews were organized to evolve the list until all experts agreed on the list. Because of the logistic complexity of coordinating the large panel, we took a grow-shrink approach. In the growing phase, all suggested items were included, even an item suggested by only 1 panelist. In the shrinking phase, any item opposed by a panelist was excluded. As it turned out, most items were initially suggested by a panelist but seconded by other panelists, suggesting the importance of the group effort for covering most important topics.

    The practical steps were developed by machine learning experts in their respective areas and finally approved by the panel. During the process, the panelists consulted extensively the broad literature on machine learning and predictive model in particular [16-18].


    A total of 4 iterations of emails resulted in the final form of the guidelines. Email 1 generated diverse responses in terms of topics. However the final scope was generally agreed upon. For email 2, most panelists commented on only a subset of topics (mostly the ones suggested by themselves). No recommendations generated significant disagreement except for minor wording decisions and quantifying conditions.

    The final results included a list of reporting items (Tables 1-5,Textboxes 1-4, and Figure 1) and a template flowchart for reporting data used for training and testing predictive models, including both internal validation and external validation (Figure 2).

    Recognizing the broad meaning of the term “machine learning,” we distinguish essential items from desirable items (using appropriate footnotes in the tables). The essential items should be included in any report, unless there is a strong reason indicating otherwise; the desirable items should be reported whenever applicable.

    Figure 1. Steps to identify the prediction problem.
    View this figure
    Table 1. Items to include when reporting predictive models in biomedical research: title and abstract.
    View this table
    Table 2. Items to include when reporting predictive models in biomedical research: introduction section.
    View this table
    Figure 2. Information flow in the predictive modelling process.
    View this figure
    Table 3. Items to include when reporting predictive models in biomedical research: methods section.
    View this table
    Table 4. Items to include when reporting predictive models in biomedical research: results section.
    View this table
    Table 5. Items to include when reporting predictive models in biomedical research: discussion section.
    View this table

    Textbox 1. Data leakage problem.
    View this box

    Textbox 2. Calibration.
    View this box

    Textbox 3. Perfect separation problem.
    View this box

    Textbox 4. K-fold cross-validation.
    View this box


    We have generated a set of guidelines that will enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research.

    Because of the broad range of machine learning methods that can be used in biomedical applications, we involved a large number of stakeholders, as either developers of machine learning methods or users of these methods in biomedicine research.

    The guidelines here cover most popular machine learning methods appearing in biomedical studies. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community.

    Although the proposed guidelines result from a voluntary effort without dedicated funding support, we still managed to assemble a panel of researchers from multiple disciplines, multiple institutions, and multiple continents. We hope the guidelines can result in more people contributing their knowledge and experience in the discussion.

    As machine learning is a rapidly developing research area, the guidelines are not expected to cover every aspect of the modeling process. The guidelines are expected to evolve as research in biomedicine and machine learning progresses.


    This project is partially supported by the Telstra-Deakin COE in Big data and Machine Learning.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Guidelines for fitting some common predictive models.

    PDF File (Adobe PDF File), 77KB

    Multimedia Appendix 2


    PDF File (Adobe PDF File), 21KB


    1. Ayaru L, Ypsilantis P, Nanapragasam A, Choi RC, Thillanathan A, Min-Ho L, et al. Prediction of outcome in acute lower gastrointestinal bleeding using gradient boosting. PLoS One 2015;10(7):e0132485 [FREE Full text] [CrossRef] [Medline]
    2. Ogutu J, Schulz-Streeck T, Piepho HP. Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. BMC Proc 2012;6(Suppl 2):S10. [CrossRef]
    3. Tran T, Luo W, Phung D, Harvey R, Berk M, Kennedy RL, et al. Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments. BMC Psychiatry 2014 Mar 14;14:76 [FREE Full text] [CrossRef] [Medline]
    4. Breiman L, Friedman J, Stone C, Olshen R. Classification and regression trees. New York: Chapman & Hall; 1984.
    5. Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science 2015 Jul 17;349(6245):255-260. [CrossRef] [Medline]
    6. Ghahramani Z. Probabilistic machine learning and artificial intelligence. Nature 2015 May 28;521(7553):452-459. [CrossRef] [Medline]
    7. Bone D, Goodwin MS, Black MP, Lee C, Audhkhasi K, Narayanan S. Applying machine learning to facilitate autism diagnostics: pitfalls and promises. J Autism Dev Disord 2015 May;45(5):1121-1136 [FREE Full text] [CrossRef] [Medline]
    8. Metaxas P, Mustafaraj E, Gayo-Avello D. How (not) to predict elections. 2011 Presented at: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing; 2011; Boston.
    9. Jungherr A, Jurgens P, Schoen H. Why the pirate party won the German election of 2009 or the trouble with predictions: a response to Tumasjan A, Sprenger TO, Sander PG, & Welpe IM “Predicting elections with Twitter: what 140 characters reveal about political sentiment”. Social Science Computer Review 2011 Apr 25;30(2):229-234. [CrossRef]
    10. Lazer D, Kennedy R, King G, Vespignani A. Big data. The parable of Google flu: traps in big data analysis. Science 2014 Mar 14;343(6176):1203-1205. [CrossRef] [Medline]
    11. Foster KR, Koprowski R, Skufca JD. Machine learning, medical diagnosis, and biomedical engineering research - commentary. Biomed Eng Online 2014 Jul 05;13:94 [FREE Full text] [CrossRef] [Medline]
    12. Smialowski P, Frishman D, Kramer S. Pitfalls of supervised feature selection. Bioinformatics 2010 Feb 1;26(3):440-443 [FREE Full text] [CrossRef] [Medline]
    13. Babyak M. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med 2004;66(3):411-421. [Medline]
    14. Hawkins DM. The problem of overfitting. J Chem Inf Comput Sci 2004;44(1):1-12. [CrossRef] [Medline]
    15. Subramanian J, Simon R. Overfitting in prediction models - is it a problem only in high dimensions? Contemp Clin Trials 2013 Nov;36(2):636-641. [CrossRef] [Medline]
    16. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference and prediction. New York, NY: Springer; 2009.
    17. Kuhn M, Johnson K. Applied predictive modeling. Berlin: Springer; 2013.
    18. Steyerberg E. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating (Statistics for Biology and Health). New York: Springer; 2009.
    19. Cox D. Two further applications of a model for binary regression. Biometrika 1958 Dec;45(3/4):562-565. [CrossRef]
    20. Quinlan J. Simplifying decision trees. Int J Man Mach Stud 1987 Sep;27(3):221-234. [CrossRef]
    21. Quinlan J. Induction of decision trees. Mach Learn 1986 Mar;1(1):81-106. [CrossRef]
    22. Podgorelec V, Kokol P, Stiglic B, Rozman I. Decision trees: an overview and their use in medicine. J Med Syst 2002 Oct;26(5):445-463. [Medline]
    23. Kotsiantis S. Decision trees: a recent overview. Artif Intell Rev 2011 Jun 29;39(4):261-283. [CrossRef]
    24. Kingsford C, Salzberg SL. What are decision trees? Nat Biotechnol 2008 Sep;26(9):1011-1013 [FREE Full text] [CrossRef] [Medline]
    25. Luo W, Gallagher M. Unsupervised DRG upcoding detection in healthcare databases. 2010 Presented at: IEEE International Conference on Data Mining Workshops (ICDMW); 2010; Sydney p. 600-605. [CrossRef]
    26. Siddique J, Ruhnke GW, Flores A, Prochaska MT, Paesch E, Meltzer DO, et al. Applying classification trees to hospital administrative data to identify patients with lower gastrointestinal bleeding. PLoS One 2015;10(9):e0138987 [FREE Full text] [CrossRef] [Medline]
    27. Bae S, Lee SA, Lee SH. Prediction by data mining, of suicide attempts in Korean adolescents: a national study. Neuropsychiatr Dis Treat 2015;11:2367-2375 [FREE Full text] [CrossRef] [Medline]
    28. Satomi J, Ghaibeh AA, Moriguchi H, Nagahiro S. Predictability of the future development of aggressive behavior of cranial dural arteriovenous fistulas based on decision tree analysis. J Neurosurg 2015 Jul;123(1):86-90. [CrossRef] [Medline]
    29. Breiman L. Random forests. Mach Learn 2001;45(1):5-32. [CrossRef]
    30. Boulesteix A, Janitza S, Kruppa J, König I. Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. WIREs Data Mining Knowl Discov 2012 Oct 18;2(6):493-507. [CrossRef]
    31. Strobl C, Malley J, Tutz G. An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol Methods 2009 Dec;14(4):323-348 [FREE Full text] [CrossRef] [Medline]
    32. Touw W, Bayjanov JR, Overmars L, Backus L, Boekhorst J, Wels M, et al. Data mining in the Life Sciences with random forest: a walk in the park or lost in the jungle? Brief Bioinform 2013 May;14(3):315-326 [FREE Full text] [CrossRef] [Medline]
    33. Asaoka R, Iwase A, Tsutsumi T, Saito H, Otani S, Miyata K, et al. Combining multiple HRT parameters using the 'Random Forests' method improves the diagnostic accuracy of glaucoma in emmetropic and highly myopic eyes. Invest Ophthalmol Vis Sci 2014 Apr 17;55(4):2482-2490. [CrossRef] [Medline]
    34. Yoshida T, Iwase A, Hirasawa H, Murata H, Mayama C, Araie M, et al. Discriminating between glaucoma and normal eyes using optical coherence tomography and the 'Random Forests' classifier. PLoS One 2014;9(8):e106117 [FREE Full text] [CrossRef] [Medline]
    35. Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Series B Stat Methodol 1996:267-288.
    36. Tibshirani R. Regression shrinkage and selection via the lasso: a retrospective. Journal of the Royal Statistical Societyries B (Statistical Methodology) 2011;73(3):273-282. [CrossRef]
    37. Vidaurre D, Bielza C, Larrañaga P. A survey of L1 regression. Int Stat Rev 2013 Oct 24;81(3):361-387. [CrossRef]
    38. Hesterberg T, Choi N, Meier L, Fraley C. Least angle and ℓ 1 penalized regression: A review. Statist Surv 2008;2:61-93. [CrossRef]
    39. Fujino Y, Murata H, Mayama C, Asaoka R. Applying “lasso” regression to predict future visual field progression in glaucoma patients. Invest Ophthalmol Vis Sci 2015 Apr;56(4):2334-2339. [CrossRef] [Medline]
    40. Shimizu Y, Yoshimoto J, Toki S, Takamura M, Yoshimura S, Okamoto Y, et al. Toward probabilistic diagnosis and understanding of depression based on functional MRI data analysis with logistic group LASSO. PLoS One 2015;10(5):e0123524 [FREE Full text] [CrossRef] [Medline]
    41. Lee T, Chao P, Ting H, Chang L, Huang Y, Wu J, et al. Using multivariate regression model with least absolute shrinkage and selection operator (LASSO) to predict the incidence of Xerostomia after intensity-modulated radiotherapy for head and neck cancer. PLoS One 2014;9(2):e89700 [FREE Full text] [CrossRef] [Medline]
    42. Friedman J. Stochastic gradient boosting. Comput Stat Data Anal 2002 Feb;38(4):367-378. [CrossRef]
    43. Natekin A, Knoll A. Gradient boosting machines, a tutorial. Front Neurorobot 2013;7:21 [FREE Full text] [CrossRef] [Medline]
    44. De'ath G. Boosted trees for ecological modeling and prediction. Ecology 2007 Jan;88(1):243-251. [Medline]
    45. Mayr A, Binder H, Gefeller O, Schmid M. The evolution of boosting algorithms. From machine learning to statistical modelling. Methods Inf Med 2014;53(6):419-427. [CrossRef] [Medline]
    46. González-Recio O, Jiménez-Montero JA, Alenda R. The gradient boosting algorithm and random boosting for genome-assisted evaluation in large data sets. J Dairy Sci 2013 Jan;96(1):614-624. [CrossRef] [Medline]
    47. Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods. Cambridge: Cambridge University Press; 2000.
    48. Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20(3):273-297. [CrossRef]
    49. Burges C. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 1998;2(2):121-167. [CrossRef]
    50. Smola A, Schölkopf B. A tutorial on support vector regression. Stat Comput 2004 Aug;14(3):199-222. [CrossRef]
    51. Vapnik V, Mukherjee S. Support Vector Method for Multivariate Density Estimation. 2000 Presented at: Neural Information Processing Systems; 1999; Denver. [CrossRef]
    52. Manevitz L, Yousef M. One-class SVMs for document classification. J Mach Learn Res 2001;2:139-154.
    53. Tsochantaridis I, Hofmann T, Altun Y. Support vector machine learning for interdependent and structured output spaces. 2004 Presented at: the twenty-first international conference on Machine learning; 2004; Banff. [CrossRef]
    54. Shilton A, Lai DT, Palaniswami M. A division algebraic framework for multidimensional support vector regression. IEEE Trans Syst Man Cybern B Cybern 2010 Apr;40(2):517-528. [CrossRef] [Medline]
    55. Shashua A, Levin A. Taxonomy of large margin principle algorithms for ordinal regression problems. Adv Neural Inf Process Syst 2002;15:937-944.

    Edited by G Eysenbach; submitted 12.04.16; peer-reviewed by J Gosling, Kfoster; comments to author 27.07.16; revised version received 04.11.16; accepted 23.11.16; published 16.12.16

    ©Wei Luo, Dinh Phung, Truyen Tran, Sunil Gupta, Santu Rana, Chandan Karmakar, Alistair Shilton, John Yearwood, Nevenka Dimitrova, Tu Bao Ho, Svetha Venkatesh, Michael Berk. Originally published in the Journal of Medical Internet Research (, 16.12.2016.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.