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How to develop machine learning models for healthcare

Nature Materials volume 18, pages 410–414 (2019)Cite this article

Rapid progress in machine learning is enabling opportunities for improved clinical decision support. Importantly, however, developing, validating and implementing machine learning models for healthcare entail some particular considerations to increase the chances of eventually improving patient care.

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Fig. 1: Examples of different phases of the translational process of developing, validating and implementing ML models for healthcare.
Fig. 2: Dataset naming convention in clinical and ML studies.

References

  1. LeCun, Y., Bengio, Y. & Hinton, G. Nature 521, 436–444 (2015).

    Article  CAS  Google Scholar 

  2. Gulshan, V. et al. JAMA 316, 2402–2410 (2016).

    Article  Google Scholar 

  3. Esteva, A. et al. Nature 542, 115–118 (2017).

    Article  CAS  Google Scholar 

  4. Krause, J. et al. Ophthalmology 125, 1264–1272 (2018).

    Article  Google Scholar 

  5. Ehteshami Bejnordi, B. et al. JAMA 318, 2199–2210 (2017).

    Article  Google Scholar 

  6. Poplin, R. et al. Nat. Biomed. Eng. 2, 158–164 (2018).

    Article  Google Scholar 

  7. Ting, D. S. W. & Wong, T. Y. Nat. Biomed. Eng. 2, 140–141 (2018).

    Article  Google Scholar 

  8. Xu, K. et al. Preprint at https://arxiv.org/abs/1502.03044 (2015).

  9. Moher, D. et al. BMJ 340, c869 (2010).

    Article  Google Scholar 

  10. Japkowicz, N. & Stephen, S. Intell. Data Anal. 6, 429–449 (2002).

    Article  Google Scholar 

  11. Rajkomar, A. et al. npj Digit. Med. 1, 18 (2018).

    Article  Google Scholar 

  12. Ren, S., He, K., Girshick, R. & Sun, J. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2017).

    Article  Google Scholar 

  13. Liu, Y. et al. Arch. Pathol. Lab. Med. https://doi.org/10.5858/arpa.2018-0147-OA (2018).

  14. Steiner, D. F. et al. Am. J. Surg. Pathol. 42, 1636–1646 (2018).

    Article  Google Scholar 

  15. De Fauw, J. et al. Nat. Med. 24, 1342–1350 (2018).

    Article  Google Scholar 

  16. Sofka, M., Milletari, F., Jia, J. & Rothberg, A. in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (eds Cardoso, J. et al.) 258–266 (Springer, 2017).

  17. Zoph, B., Vasudevan, V., Shlens, J. & Le, Q. V. Preprint at https://arxiv.org/abs/1707.07012 (2017).

  18. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. & Chen, L.-C. in IEEE Conference on Computer Vision and Pattern Recognition 4510–4520 (IEEE, 2018).

  19. Bishop, C. Pattern Recognition and Machine Learning (Springer, 2006).

  20. Zhang, C., Bengio, S., Hardt, M., Recht, B. & Vinyals, O. Preprint at https://arxiv.org/abs/1611.03530 (2016).

  21. Bergstra, J. & Bengio, Y. J. Mach. Learn. Res. 13, 281–305 (2012).

    Google Scholar 

  22. ILSVRC http://www.image-net.org/challenges/LSVRC/announcement-June-2-2015 (2 June 2015).

  23. Alba, A. C. et al. JAMA 318, 1377–1384 (2017).

    Article  Google Scholar 

  24. Niculescu-Mizil, A. & Caruana, R. in Proc. 22nd International Conference on Machine Learning 625–632 (ACM, 2005).

  25. Thabane, L. et al. BMC Med. Res. Methodol. 13, 92 (2013).

    Article  Google Scholar 

  26. Parikh, R., Mathai, A., Parikh, S., Chandra Sekhar, G. & Thomas, R. Indian J. Ophthalmol. 56, 45–50 (2008).

    Article  Google Scholar 

  27. van Smeden, M., Van Calster, B. & Groenwold, R. H. H. JAMA 319, 1725–1726 (2018).

    Article  Google Scholar 

  28. Sayres, R. et al. Ophthalmology 126, 552–564 (2018).

    Article  Google Scholar 

  29. Graham, K. C. & Cvach, M. Am. J. Crit. Care 19, 28–34 (2010).

    Article  Google Scholar 

  30. Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N. & Folk, J. C. npj Digit. Med. 1, 39 (2018).

    Article  Google Scholar 

  31. Shlens, J. Google AI Blog https://ai.googleblog.com/2016/03/train-your-own-image-classifier-with.html (2016).

Download references

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Author notes

  1. These authors contributed equally: Po-Hsuan Cameron Chen, Yun Liu.

Authors and Affiliations

  1. Google AI Healthcare, Mountain View, CA, USA

    Po-Hsuan Cameron Chen, Yun Liu & Lily Peng

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  1. Po-Hsuan Cameron Chen
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  2. Yun Liu
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Correspondence to Po-Hsuan Cameron Chen.

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Chen, PH.C., Liu, Y. & Peng, L. How to develop machine learning models for healthcare. Nat. Mater. 18, 410–414 (2019). https://doi.org/10.1038/s41563-019-0345-0

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