An Architecture for Cloud-Assisted Clinical Support System for Patient Monitoring and Disease Detection In Mobile Environments
Bibliographic record
Abstract
Opportunities exist to improve healthcare delivery by extending to the domain of mobile healthcare the emerging clinical support system that facilitates patient monitoring and early disease detection. Current research focuses on hospitallevel patient monitoring using an automated clinical support system. A similar system can be adapted to support medical monitoring and remote care for mobile patients. Unlike the hospital environment where stationary computing infrastructures can be leveraged for the medical data acquisition, processing and storage, the mobile healthcare environment may rely solely on mobile devices, such as a smartphone, to acquire and process the medical data. Given that the mobile devices are constrained by energy, processing and storage capabilities, outsourcing some of the operations to the cloud is a plausible approach. Cloud-assisted clinical support system for mobile patients creates more opportunities for healthcare delivery, but there are attendant challenges that must be considered in the development of the system. This paper identifies these opportunities and the challenges that exist with the development of cloud-assisted clinical support system for patient monitoring and disease detection in mobile environments, and introduces an architecture for developing the system, with a proof of concept.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".