iVS: an intelligent end-to-end vital sign capture platform using smartphones
Bibliographic record
Abstract
This paper presents the design and implementation of an intelligent vital sign capture platform, namely iVS, which provides end-to-end connectivity between patient monitors and Electronic Healthcare Record (EHR) by using smartphones. By replacing manual operations with automatic machine-tomachine (M2M) communications, iVS aims to enhance reliability, save time and costs for patient's monitoring routines carried out in hospitals, clinics and emergency sites. Using advanced wireless communication technologies, it also allows medical staff to access to patient health conditions and other information (e.g., medications, prescriptions, medical treatment history, etc.) from EHR from anywhere at anytime in order to have fast and efficient responses to emergency situations. Network architecture and system design of iVS are first presented. M2M communication protocols to enable inter-operable data exchange between various system entities are explained. Theoretical maximum user data throughput that can be obtained over the Bluetooth Low Energy (BLE) communication link connecting patient monitors and smartphones is also calculated. Experiments results are also presented and discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".