Federated learning: a step in the right direction to improve data equity.
作者信息
van Genderen Michel E, van de Sande Davy, Cecconi Maurizio, Jung Christian
机构信息
Department of Adult Intensive Care, Erasmus MC, University Medical Center Rotterdam, Internal Postadress-Room Ne-403, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.
Biomedical Sciences Department, Humanitas University, Milan, Italy.
出版信息
Intensive Care Med. 2024 Aug;50(8):1393-1394. doi: 10.1007/s00134-024-07525-1. Epub 2024 Jul 2.
Abstract
摘要
相似文献
1
Federated learning: a step in the right direction to improve data equity.
Intensive Care Med. 2024 Aug;50(8):1393-1394. doi: 10.1007/s00134-024-07525-1. Epub 2024 Jul 2.
2
Architectural Design of a Blockchain-Enabled, Federated Learning Platform for Algorithmic Fairness in Predictive Health Care: Design Science Study.
J Med Internet Res. 2023 Oct 30;25:e46547. doi: 10.2196/46547.
3
Federated Learning With Taskonomy for Non-IID Data.
IEEE Trans Neural Netw Learn Syst. 2022 Mar 22;PP. doi: 10.1109/TNNLS.2022.3152581.
4
A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals.
Lancet Digit Health. 2024 Feb;6(2):e93-e104. doi: 10.1016/S2589-7500(23)00226-1.
5
The need for multimodal health data modeling: A practical approach for a federated-learning healthcare platform.
J Biomed Inform. 2023 May;141:104338. doi: 10.1016/j.jbi.2023.104338. Epub 2023 Apr 5.
6
Federated Quantum Machine Learning.
Entropy (Basel). 2021 Apr 13;23(4):460. doi: 10.3390/e23040460.
7
Secure and decentralized federated learning framework with non-IID data based on blockchain.
Heliyon. 2024 Feb 29;10(5):e27176. doi: 10.1016/j.heliyon.2024.e27176. eCollection 2024 Mar 15.
8
Accessible Ecosystem for Clinical Research (Federated Learning for Everyone): Development and Usability Study.
JMIR Form Res. 2024 Jul 17;8:e55496. doi: 10.2196/55496.
9
Challenges and future directions of secure federated learning: a survey.
Front Comput Sci (Berl). 2022;16(5):165817. doi: 10.1007/s11704-021-0598-z. Epub 2021 Dec 10.
10
Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches.
Sci China Life Sci. 2022 Mar;65(3):529-539. doi: 10.1007/s11427-021-1946-0. Epub 2021 Jul 26.
引用本文的文献
1
How AI can help in error detection and prevention in the ICU?
Intensive Care Med. 2025 Mar;51(3):590-592. doi: 10.1007/s00134-024-07775-z. Epub 2025 Jan 22.
2
Trash in/trash out? Using routinely collected clinical data for data science in the ICU: Con.
Intensive Care Med. 2025 Feb;51(2):382-384. doi: 10.1007/s00134-024-07739-3. Epub 2024 Dec 23.
3
Frailty is crucial in FORECASTing outcomes in critical care.
Intensive Care Med. 2024 Jul;50(7):1119-1122. doi: 10.1007/s00134-024-07518-0. Epub 2024 Jul 2.
本文引用的文献
1
Why federated learning will do little to overcome the deeply embedded biases in clinical medicine.
Intensive Care Med. 2024 Aug;50(8):1390-1392. doi: 10.1007/s00134-024-07491-8. Epub 2024 Jun 3.
2
Charting a new course in healthcare: early-stage AI algorithm registration to enhance trust and transparency.
NPJ Digit Med. 2024 May 8;7(1):119. doi: 10.1038/s41746-024-01104-w.
3
Federated data access and federated learning: improved data sharing, AI model development, and learning in intensive care.
Intensive Care Med. 2024 Jun;50(6):974-977. doi: 10.1007/s00134-024-07408-5. Epub 2024 Apr 18.
4
Tackling bias in AI health datasets through the STANDING Together initiative.
Nat Med. 2022 Nov;28(11):2232-2233. doi: 10.1038/s41591-022-01987-w.
5
Systematic Review and Comparison of Publicly Available ICU Data Sets-A Decision Guide for Clinicians and Data Scientists.
Crit Care Med. 2022 Jun 1;50(6):e581-e588. doi: 10.1097/CCM.0000000000005517. Epub 2022 Mar 2.
6
Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit.
Intensive Care Med. 2021 Jul;47(7):750-760. doi: 10.1007/s00134-021-06446-7. Epub 2021 Jun 5.
Zotero 插件