The sharing of electronic healthcare records (EHRs) is important to healthcare and medical research. However, institutions are faced with difficulties in privacy protection and efficiently secure data exchange. The main objective of this study is to propose a controllable secure blockchain-based EHRs sharing scheme. For this purpose, blockchain technologies are combined with interplanetary file systems (IPFS) to provide efficient secure EHRs sharing. Firstly, the IPFS-based EHR file system (IEFS) is designed to save and share large-size EHR files among medical institutions. With the high-throughput content-addressed block storage model and appropriate redundant backup of IPFS, IEFS is tamper-resistant and free of a single point of failure. Secondly, the blockchain is used to implement the blockchain-based EHR abstract system (BEAS) to manipulate EHR abstracts access. In BEAS, the EHR file addresses generated by IEFS are encrypted and saved in EHR abstracts for privacy protection. Since EHR abstracts are encrypted by patients’ public keys, the sharing of EHR files is under the control of patients. In our experiment, a prototype system is developed to validate the proposed scheme. The experimental results showed that (1) EHRs are securely shared under the control of patients and (2) EHR files are retrieved at an acceptable speed supported by IPFS technology. In this paper, solutions to some important practical issues such as incapacitated patients, encryption key forgetting/missing, and efficient interaction of doctors with EHRs sharing scheme are also seriously discussed.

Multimodal named entity recognition (MNER) aims to improve entity detection in social media texts by leveraging accompanying images, but its performance is often affected by weak text–image alignment, noisy or irrelevant visual content, and limited separation among entity representations. To address these issues, this study proposes CUA-MNER, a contrastive uncertainty–aware framework that combines hierarchical vision–text alignment, variational uncertainty–aware fusion, and token-level contrastive learning. The alignment module models correspondences at token, phrase, and sentence levels, allowing local visual regions and global image context to support textual entity recognition. The fusion module estimates epistemic and aleatoric uncertainty through variational inference and adaptively adjusts the contribution of each modality for different samples. The contrastive objective further encourages entity representations of the same type to be closer while separating different entity types. Experiments on the Twitter2015 and Twitter2017 benchmarks demonstrate that CUA-MNER achieves F1 scores of 76.97% and 89.66%, respectively, outperforming competitive baselines by 0.66 and 1.95 F1 points. Ablation and diagnostic analyses show that the three components provide complementary benefits. These results suggest that modeling modality reliability is useful for robust MNER, while the additional modules also introduce computational overhead and leave cross-domain generalization as an open issue.