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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".