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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.

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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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  3. Lily Peng
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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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