oHealth: Opportunistic Healthcare in Public Transit through Fog and Edge Computing
Bibliographic record
Abstract
Access to healthcare services is one of the fundamental pillars of society today. People either have access to healthcare or they are not privileged enough to visit a doctor. In underprivileged societies, access to healthcare is difficult due to various reasons such as fewer number of doctors and healthcare facilities, inefficient communication means, and so on. This may result in exaggeration of the disease or threat to life. On the other hand, those who are privileged, end up paying unnecessary visits to the physician in most of the cases on an average (>71% in USA), and incurring high costs. Often-times, health issues are minor, and it is not necessary to visit a doctor. In such a case, the patient only requires suggestions on quick health fix, or some precautionary measures. Such information can be provided through the combination of a smartphone-based app, fog/edge computing, and mobile communication. Hence, reaching out to the doctors in their available time (off-time, commuting via public transit, so on) when they can answer some quick questions is one of the solutions that fit into smart healthcare definition, and truly pervasive healthcare. In this paper, we propose an innovative healthcare service architecture, called opportunistic healthcare (oHealth), where health log and questions can be opportunistically offloaded to a nearby (such as at public transit) compute entity (such as fog server) capable of processing health logs (up to 234,729 logs daily as per our setup). The fog node communicates the questions to a doctor, who provides the necessary feedback. We provide proof-of-concept evaluation results (in WiFi vs 4G networks, on the basis of delay, cost, and commute time, comparing cloud with fog) to endorse the applicability of our oHealth.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".