A Cloud-Edge-Aided Incremental High-Order Possibilistic c-Means Algorithm for Medical Data Clustering
Bibliographic record
Abstract
Medical Internet of Things are generating a big volume of data to enable smart medicine that tries to offer computer-aided medical and healthcare services with artificial intelligence techniques like deep learning and clustering. However, it is a challenging issue for deep learning, and clustering algorithms to analyze large medical data because of their high computational complexity, thus hindering the progress of smart medicine. In this article, we present an incremental high-order possibilistic c-means algorithm (IHoPCM) on a cloud-edge computing system to achieve medical data coclustering of multiple hospitals in different locations. Specifically, each hospital employs the deep computation model to learn a feature tensor of each medical data object on the local edge computing system, and then uploads the feature tensors to the cloud computing platform. The high-order possibilistic c-means algorithm is performed on the cloud system for medical data clustering on uploaded feature tensors. Once the new medical data feature tensors are arriving at the cloud computing platform, the incremental high-order possibilistic c-means algorithm (IHoPCM) is performed on the combination of the new feature tensors and the previous clustering centers to obtain clustering results for the feature tensors received to date. In this way, repeated clustering on the previous feature tensors is avoided to improve the clustering efficiency. In the experiments, we compare different algorithms on two medical datasets regarding clustering accuracy and clustering efficiency. Results show that the presented IHoPCM method achieves great improvements over the compared algorithms in clustering accuracy and efficiency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".