WebLee et al. and Xu et al. presented two federated patient hashing frameworks for patient similarity learning. The model learns context-specific hash codes to represent patients across multiple hospitals. The learned hash codes are then used to calculate similarities among patients. Ultimately, the model can match patients with high similarity ... WebAug 7, 2024 · IPFS uses hashes to identify people, and blockchain has traceability, so when a system is compromised, it can be traced to the person who broke it. Extensibility: In order to ensure that the blockchain is open, transparent and non-tamperable, any node must be given equal rights and obligations.
Jie Xu - Google Scholar
WebApr 3, 2024 · This paper proposes a Federated Patient Hashing (FPH) framework, which collaboratively trains a retrieval model stored in a shared memory while keeping all the … WebJan 1, 2024 · Federated learning(aka collaborative learning) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers … metabo hpt battery troubleshooting
Privacy-Preserving Patient Similarity Learning in a Federated ...
WebJul 12, 2024 · This paper proposes a Federated Patient Hashing (FPH) framework, which collaboratively trains a retrieval model stored in a shared memory while keeping all the patient-level information in local institutions and analyzes the convergence rate of the FPH framework. 10 PDF View 3 excerpts, references background Supervised hashing with … WebApr 15, 2024 · As an example, a privacy-preserving federated patient hashing framework for learning patient similarity across institutions has been presented by Lee et al. . … WebApr 3, 2024 · Federated Patient Hashing @inproceedings{Xu2024FederatedPH, title={Federated Patient Hashing}, author={Jie Xu and Zhenxing Xu and Peter B. Walker and Fei Wang}, booktitle={AAAI}, year={2024} } Jie Xu, Zhenxing Xu, +1 author Fei Wang; Published in AAAI 3 April 2024; Computer Science how tall kurt cobain