ISO/IEEE 11073 Personal Health Device (X73-PHD) Standards Compliant Systems: A Systematic Literature Review
Bibliographic record
Abstract
Since the introduction of the ISO/IEEE 11073 personal health device (X73-PHD) standards, as part of ISO/IEEE 11073 family of standards, it has been applied to many health systems developed for personal use. In this systematic literature review, we review existing literature collected using three databases: Scopus, Pub Med, and Web of Science. We propose a classification for personal health systems based on the location in which they are used, the technology used to develop them, and the purpose which is determined by the targeted users. We found 51% of the devices used in such systems are standardized while approximately 40% are not and five systems did not specify the device status (9%). Various adaption techniques were used for standardization. Besides, the pulse oximeter is the most used device in such systems since it was used in 43% of them. In addition, we present the role of the X73-PHD standards in the Internet of Things (IoT) and tele-healthcare systems, discuss the challenges of utilizing this set of standards in health monitoring systems and converting the non-standardized devices into standardized ones. Finally, we propose the requirements of personal health systems based on our review of the literature.
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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 | 0.020 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.038 | 0.027 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".