Blockchain-Powered Tensor Meta-Learning-Driven Intelligent Healthcare System With IoT Assistance
Bibliographic record
Abstract
The rapid development and gradual integration of artificial intelligence and the Internet of Things have brought unprecedented opportunities for radically changing healthcare and treatments. However, the burgeoning in intelligent healthcare systems is severely bounded by data privacy and the security of AI models. Meanwhile, the limited local data forces conventional AI models to face the predicament in achieving personalized healthcare. Hence, we propose a blockchain-powered tensor meta-learning-driven intelligent healthcare system with IoT assistance. IoT devices as light nodes upload the local shareable data to the edge server(full node) for model training and perform the local private data by non-tampered model downloaded via smart contract. The system can not only use blockchain technology to ensure the strong consistency of the healthcare model but also protect private data from being leaked. Especially, we develop a tensor meta-learning model named tensor-prototype graph network to achieve efficient modeling of heterogeneous healthcare data. Building on the tensors and graph network, the model is conducive to capturing the data distribution when there are few labeled data. To evaluate our proposed approach, we have conducted experiments on three classic databases. The results demonstrate that our approach is capable of effectively promoting the performance of intelligent healthcare.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".