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| Feasibility Study of Federated Learning on the Distributed Research Network of OMOP Common Data Model |
| Geun Hyeong Lee, Jonggul Park, Jihyeong Kim, Yeesuk Kim, Byungjin Choi, Rae Woong Park, Sang Youl Rhee, Soo-Yong Shin |
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Healthc Inform Res. 2023;29(2):168-173. Published online April 30, 2023 DOI: https://doi.org/10.4258/hir.2023.29.2.168 |
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Feasibility Study of Federated Learning on the Distributed Research Network of OMOP Common Data Model Healthcare Informatics Research. 2023;29(2):168-173  Feasibility of Mapping Austrian Health Claims Data to the OMOP Common Data Model Journal of Medical Systems. 2019;43(10): The OMOP Common Data Model in Australian Primary Care Data: Building a Quality Research Ready Harmonised Dataset . 2023; Converting to a Common Data Model: What is Lost in Translation? Drug Safety. 2014;37(11):893-896 Split Learning: A Resource Efficient Model and Data Parallel Approach for Distributed Deep Learning Federated Learning. 2022;439-451 Correction to: Transforming and evaluating the UK Biobank to the OMOP Common Data Model for COVID-19 research and beyond Journal of the American Medical Informatics Association. 2023;30(5):1006-1006 Federated Learning Meets Blockchain: a Power Consumption Case Study 2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). 2023; Fidelity Assessment of a Clinical Practice Research Datalink Conversion to the OMOP Common Data Model Drug Safety. 2014;37(11):945-959 Distributed Learning in Trusted Execution Environment: A Case Study of Federated Learning in SGX 2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC). 2021; Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources . 2022; |
