Reasoning with Contextual Data in Telehealth Applications
Bibliographic record
Abstract
The notion of context-awareness is making its way in the area of mobile-health application designed to assist patients outside a hospital and medical professionals within a hospital. Such ubiquitous computing applications go beyond simple biomedical data-collection so the management of the context becomes more challenging, and the complexity of the context-based decision-algorithms warrants the use of full-blown inference engines. The context is a very volatile notion, so building a context-aware system means more than just creating a context ontology and defining inference rules for that model. This paper proposes mechanisms to dynamically manage the contextual decisions and actions. It devises a model and its properties for the process of taking each individual context-based decision. An architecture centered around this model is defined, integrating an off-the-shelf inference engine that reasons on context-data expressed in the OWL language. The architecture was refined and validated through the implementation of a prototype remote biomonitoring system. The prototype combines context-adaptation and awareness with the ubiquity of 3G network-coverage to allow for more personalized monitoring and better streamlining of information between the various health care players. It can be easily adapted and extended with extra inference-rules, additional actions, and more context items.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".