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.
References
LeCun, Y., Bengio, Y. & Hinton, G. Nature 521, 436–444 (2015).
Gulshan, V. et al. JAMA 316, 2402–2410 (2016).
Esteva, A. et al. Nature 542, 115–118 (2017).
Krause, J. et al. Ophthalmology 125, 1264–1272 (2018).
Ehteshami Bejnordi, B. et al. JAMA 318, 2199–2210 (2017).
Poplin, R. et al. Nat. Biomed. Eng. 2, 158–164 (2018).
Ting, D. S. W. & Wong, T. Y. Nat. Biomed. Eng. 2, 140–141 (2018).
Xu, K. et al. Preprint at https://arxiv.org/abs/1502.03044 (2015).
Moher, D. et al. BMJ 340, c869 (2010).
Japkowicz, N. & Stephen, S. Intell. Data Anal. 6, 429–449 (2002).
Rajkomar, A. et al. npj Digit. Med. 1, 18 (2018).
Ren, S., He, K., Girshick, R. & Sun, J. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2017).
Liu, Y. et al. Arch. Pathol. Lab. Med. https://doi.org/10.5858/arpa.2018-0147-OA (2018).
Steiner, D. F. et al. Am. J. Surg. Pathol. 42, 1636–1646 (2018).
De Fauw, J. et al. Nat. Med. 24, 1342–1350 (2018).
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).
Zoph, B., Vasudevan, V., Shlens, J. & Le, Q. V. Preprint at https://arxiv.org/abs/1707.07012 (2017).
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. & Chen, L.-C. in IEEE Conference on Computer Vision and Pattern Recognition 4510–4520 (IEEE, 2018).
Bishop, C. Pattern Recognition and Machine Learning (Springer, 2006).
Zhang, C., Bengio, S., Hardt, M., Recht, B. & Vinyals, O. Preprint at https://arxiv.org/abs/1611.03530 (2016).
Bergstra, J. & Bengio, Y. J. Mach. Learn. Res. 13, 281–305 (2012).
ILSVRC http://www.image-net.org/challenges/LSVRC/announcement-June-2-2015 (2 June 2015).
Alba, A. C. et al. JAMA 318, 1377–1384 (2017).
Niculescu-Mizil, A. & Caruana, R. in Proc. 22nd International Conference on Machine Learning 625–632 (ACM, 2005).
Thabane, L. et al. BMC Med. Res. Methodol. 13, 92 (2013).
Parikh, R., Mathai, A., Parikh, S., Chandra Sekhar, G. & Thomas, R. Indian J. Ophthalmol. 56, 45–50 (2008).
van Smeden, M., Van Calster, B. & Groenwold, R. H. H. JAMA 319, 1725–1726 (2018).
Sayres, R. et al. Ophthalmology 126, 552–564 (2018).
Graham, K. C. & Cvach, M. Am. J. Crit. Care 19, 28–34 (2010).
Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N. & Folk, J. C. npj Digit. Med. 1, 39 (2018).
Shlens, J. Google AI Blog https://ai.googleblog.com/2016/03/train-your-own-image-classifier-with.html (2016).
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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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DOI: https://doi.org/10.1038/s41563-019-0345-0
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