|Title:||Federated Learning for Electronic Health Records||Authors:||Dang, Trung Kien
|Issue Date:||2022||Publisher:||Association for Computing Machinery (ACM)||Citation:||Dang, Trung Kien, Lan Xiang, Weng Jianshu, Feng, Mengling (2022). Federated Learning for Electronic Health Records. ACM Transactions on Intelligent Systems and Technology. ScholarBank@NUS Repository. https://doi.org/10.1145/3514500||Abstract:||
In data-driven medical research, multi-center studies have long been preferred over single-center ones due to a single institute sometimes not having enough data to obtain sufficient statistical power for certain hypothesis testings as well as predictive and subgroup studies. The wide adoption of electronic health records (EHRs) has made multi-institutional collaboration much more feasible. However, concerns over infrastructures, regulations, privacy and data standardization present a challenge to data sharing across healthcare institutions. Federated Learning (FL), which allows multiple sites to collaboratively train a global model without directly sharing data, has become a promising paradigm to break the data isolation. In this study, we surveyed existing works on FL applications in EHRs and evaluated the performance of current state-of-the-art FL algorithms on two EHR machine learning tasks of significant clinical importance on a real world multi-center EHR dataset.
|Source Title:||ACM Transactions on Intelligent Systems and Technology||URI:||https://scholarbank.nus.edu.sg/handle/10635/226684||ISSN:||21576904
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