RESTful dissemination of healthcare data in mobile digital ecosystem
Bibliographic record
Abstract
Mobile technology is playing a significant role in transforming the healthcare domain and enabling a new era of digital healthcare ecosystem. In healthcare, tablets are replacing the conventional paper-based way of tracking patient's record. These devices are not only used to collect user's inputs as events, they can perform various analytical computations and provide an instant output in order to meet investigator's need. As these tablets are interacting over wireless networks, the communication often suffers intermittent connection loss. This can prevent tablets from successfully propagating event data. In this paper, we propose a novel architecture for disseminating events among mobile participants. Our architecture has two contributions - first, it addresses the challenge of Wi-Fi network while synchronizing data among tablets, and second, it develops a RESTful architecture assuming that only HTTP like protocol is used in event dissemination. Our framework adopts PInGO (Pain Information on the Go) application that has been developed in research collaboration with Bioinformatics Research Lab at University of Saskatchewan for Juvenile Idiopathic Arthritis (JIA) patients. Patient's inputs from the application were used in disseminating event data within the proposed framework.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.003 |
| 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".