A Blockchain-enabled Intelligent Electronic Healthcare Platform
Bibliographic record
Abstract
Healthcare frameworks have evolved over the decades with the advancements in technology. Patient disease prevention, diagnosis, treatment, and well-being in general have progressed tremendously with the support of Artificial Intelligence (AI), data science, and advances in networking and communication. The security of the healthcare system plays an important role in the robustness and integrity of patient healthcare records. The use of intrusion detection and prevention plays a pivotal role in securing not only healthcare systems but also electronic health records. Today, traditional healthcare security frameworks that rely on conventional Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) cannot cope with the vulnerability of complex cyber attacks that can jeopardize private patient information. Furthermore, healthcare frameworks are shifting towards distributed and decentralized architectures that conventional IDS and IPS solutions are incapable of protecting. With that said, in this paper we present a blockchain-enabled IDS framework to protect electronic healthcare systems, which uses a diversified set of centralized, distributed, and decentralized security measures to detect and prevent a plethora of cyber attacks. The system is designed to be adaptable and capable of finding new and developing threats. A data set is simulated on the basis of healthcare flows, access levels, and reason. The evaluation results show that the proposed IDS framework provides adequate results in improving security.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".