書誌事項
- 公開日
- 2020-09-14
- 権利情報
-
- https://creativecommons.org/licenses/by/4.0
- https://creativecommons.org/licenses/by/4.0
- DOI
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- 10.1038/s41746-020-00323-1
- 公開者
- Springer Science and Business Media LLC
説明
<jats:title>Abstract</jats:title><jats:p>Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.</jats:p>
収録刊行物
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- npj Digital Medicine
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npj Digital Medicine 3 (1), 119-, 2020-09-14
Springer Science and Business Media LLC