Ontology Support for Healthcare IoT
Bibliographic record
Abstract
Ontology is necessary in medical information systems to manage terminologies and explain unambiguously medical concepts. Healthcare Internet of Things is providing large scale connectivity for medical devices, diseases, concepts, drugs, vaccines, and procedures for humans such as physicians and clinical staff to use them. Because of distributed connectivity of the “things”, remotely administering medical help to people, wherever they are and whenever they want, is possible. It is in this context health care domain Ontology plays a crucial role, in providing a sound semantic basis for defining the meaning, relatedness, and procedures to use the “things” for data integration and analysis in health care domain. In general, there may exist more than one ontology for a specific application domain and concept terms may be distributed among them. The degree of closeness of semantically related concept terms that are distributed among different ontologies in a specific domain is usually called semantic similarity of concept terms in that domain. Both structural uniformity and semantic compatibility are essential for Healthcare IoT. This paper proposes a formal structural representation for an Ontology that enables a formal approach to efficiently estimate similarity measures between concept terms from different ontologies in the same domain, without merging the ontologies. The measures estimated by the proposed method have stability property, where stability means the relative ordering based on the estimated measures will be consistent with the ordering produced by measures calculated from the merged ontologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".