A Component Based Framework to Enable Medical Devices Communication
Bibliographic record
Abstract
The interconnection of medical devices is emerging as a new requirement in modern medicine. The final goal of connecting heterogeneous medical devices in a wider network of computational servers is to monitor and improve patient safety, where it also constitutes a major goal in the Integrated Clinical Environment (ICE) framework. The heterogeneity of medical devices provided by different suppliers is a key challenge in ICE-based systems, where interoperability and data communication across devices is still under study and specification. ICE aims to create a standard interface that covers medical devices heterogeneity, hence, achieving interoperability in a safe way. It focuses on defining an interoperable bus between the patient, medical devices, software applications, and the clinician. Given the lack of realization of ICE standard, this paper presents a component-based framework for making ICE usable for medical applications. This work illustrates the component model in detail and validates it with a prototype implementation that focuses on the integration of heterogeneous medical devices as the most relevant requirements faced by ICE.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".