Developing federated time-to-event scores using heterogeneous real-world survival data
This study demonstrates the effectiveness of our privacy-preserving federated survival score generation framework and its applicability to real-world heterogeneous survival data.
Authors
- Bibhas Chakraborty (ORCID: https://orcid.org/0000-0002-7366-0478)
- Yilin Ning (ORCID: https://orcid.org/0000-0002-6758-4472)
- Chuan Hong (ORCID: https://orcid.org/0000-0001-7056-9559)
- Qiming Wu (ORCID: https://orcid.org/0000-0002-0413-9216)
- Marcus Eng Hock Ong (ORCID: https://orcid.org/0000-0001-7874-7612)
- Siqi Li (ORCID: https://orcid.org/0000-0003-4026-963X)
- Ziwen Wang (ORCID: https://orcid.org/0000-0001-9798-7021)
- Yuqing Shang (ORCID: https://orcid.org/0009-0003-3249-4869)
- Nan Liu (ORCID: https://orcid.org/0000-0002-9838-4115)
- Di Miao (ORCID: https://orcid.org/0009-0008-5381-9198)
Institutions
- SingHealth (SG)
- Duke University (US)
- Singapore General Hospital (SG)
- Duke-NUS Medical School (SG)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2025-09-22
- DOI
- https://doi.org/10.1016/j.compbiomed.2025.111084
- Primary Topic
- Context-Aware Activity Recognition Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00