Bibliographic record
Abstract
The medical domain is embracing mobile technology (mHealth) to enable healthcare professionals have ubiquitous access to the Electronic Health Records (EHR). This creates the need for supporting mobile users to access medical data remotely in real-time, especially in mission critical and decision making situations. However, 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. The mobility of the healthcare professionals also poses threats to privacy especially when personalized devices such as smartphones get into the wrong hands. In this work, we focus on the above highlighted challenges by proposing a middleware-oriented mobile cloud computing framework that facilitates near real-time data propagation in the mobile environment. Also, we explore privacy and security options for the accessibility of the medical data in a mobile environment based on provenance and data transformation. The privacy is enhanced through the implementation of policies which ensures that the requester of the medical record is the intended user. The evaluation of the system, called Med App, shows that medical data dissemination can be achieved efficiently and securely.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".