DeepHealth: A Secure Framework to Manage Health Certificates Through Medical IoT, Blockchain and Deep Learning
Bibliographic record
Abstract
In order to flatten the curve and lower human-to-human transmission of COVID-19 pathogen, one of the critical suggestions by health professionals is to monitor COVID-19 virus status of each human dynamically which is not a pragmatic solution unless the COVID-19 positive, negative, or symptomatic subjects are identified and have a secure health certificate generated based on daily health status. In this paper, we have developed a Blockchain and off-chain based secure health status and user biometric storage system. The health status is being visualized through a distributed QR code app. We have also incorporated deep learning-based face recognition and QR code recognition system in which the facial features are mapped to the QR code of a subject. We have developed three distributed apps (dApps): for the citizens, hospital authorities, and COVID-19 status checking entities. The system allows, for example, supermarkets, malls, and airports, to inquire about the health status of any subject through our developed application using already installed cameras. Our system will allow full life-cycle of the health certificate and biometric user management: creation through dApps, secure storage at Blockchain and off-chain, privacy-preserving sharing with the community of interest, and dynamic visualization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".