Efficient and Privacy-Preserving Medical Research Support Platform Against COVID-19: A Blockchain-Based Approach
Bibliographic record
Abstract
COVID-19 is a major global public health challenge and difficult to control in a short time completely. To prevent the COVID-19 epidemic from continuing to worsen, global scientific research institutions have actively carried out studies on COVID-19, thereby effectively improving the prevention, monitoring, tracking, control, and treatment of the epidemic. However, the COVID-19 electronic medical records (CEMRs) among hospitals worldwide are managed independently. With privacy consideration, CEMRs cannot be made public or shared, which is not conducive to in-depth and extensive research on COVID-19 by medical research institutions. In addition, even if new research results are developed, the disclosure and sharing process is slow. To address this issue, we propose a blockchain-based medical research support platform, which can provide efficient and privacy-preserving data sharing against COVID-19. First, hospitals and medical research institutions are treated as nodes on the alliance chain, so consensus and data sharing among the nodes is achieved. Then, COVID-19 patients, doctors, and researchers need to be authenticated in various institutes. Moreover, doctors and researchers need to be registered with the Fabric certificate authority. The CEMRs for COVID-19 patients uses the blockchain's pseudonym mechanism to protect privacy. After that, doctors upload CEMRs on the alliance chain, and researchers can obtain CEMRs from the alliance chain for research. Finally, the research results will be published on the blockchain for doctors to use. The experimental results show that the read and write performance and security performance on the alliance chain meet the requirements, which can promote the wide application of scientific research results against COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".