Composition of the Electronic Health Record: Mobile Efficiency in mHealth
Bibliographic record
Abstract
Recently, there is growing research interest in the area of mHealth which aims at extending the Electronic Health Records (EHR) accessibility to the mobile node. This era facilitates real-time access to medical data which is also crucial for remote healthcare delivery. However, as reported in previous studies, supporting real-time access and services synchronization in highly distributed mobile environments can be challenging due to the presence of the following factors: 1) sporadic wireless disconnections, 2) fluctuating bandwidth, 3) variations in device features, and 4) battery life constraints. In this paper, we aim to enhance the medical data exchanges within the mHealth system efficiently through network adaptation. First, since the medical data is made of different web services (e.g., REST and SOAP), services composition mechanism is proposed that transforms all the web services to REST. Second, since the services processing can be intensive on the mobile, we proposed the process offloading to the cloud based on a policy that determines the availability of network bandwidth. The pilot testing of our work shows that the EHR can be accessed in real time and the system is also scalable.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".