An Ontological Approach for Early Detection of Suspected COVID-19 among COPD Patients
Bibliographic record
Abstract
Recent studies on chronic obstructive pulmonary disease (COPD) patients in the context of the coronavirus 19 (COVID-19) pandemic have reported two important problems, i.e., high mortality and vulnerability among COPD patients vs. non-COPD patients. The high number of deaths are caused by exacerbations, COVID-19, and other comorbidities. Therefore, the purpose of this article is to reduce the risk factors of COPD in the COVID-19 context. In this article, we propose approaches based on adaptation mechanisms for detecting COVID-19 symptoms, to better provide appropriate care to COPD patients. To achieve this goal, an ontological model called SuspectedCOPDcoviDOlogy has been created, which consists of five ontologies for detecting suspect cases. These ontologies use vital sign parameters, symptom parameters, service management, and alerts. SuspectedCOPDcoviDOlogy enhances the COPDology proposed by a previous research project in the COPD domain. To validate the solution, an experimental study comparing the results of an existing test for the detection of COVID-19 with the results of the proposed detection system is conducted. Finally, with these results, we conclude that a rigorous combination of detection rules based on the vital sign and symptom parameters can greatly improve the dynamic detection rate of COPD patients suspected of having COVID-19, and therefore enable rapid medical assistance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".